Sinha, Pankaj and Verma, Aniket and Shah, Purav and Singh, Jahnavi and Panwar, Utkarsh (2020): Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression.

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Abstract
This paper aims at determining the various economic and noneconomic factors that can influence the voting behaviour in the forthcoming United States Presidential Election using Lasso regression, a Machine learning algorithm. Even though contemporary discussions on the subject of the United States Presidential Election suggest that the level of unemployment in the economy will be a significant factor in determining the result of the election, in our study, it has been found that the rate of unemployment will not be the only significant factor in forecasting the election. However, various other economic factors such as the inflation rate, rate of economic growth, and exchange rates will not have a significant influence on the election result. The June Gallup Rating, is not the only significant factor for determining the result of the forthcoming presidential election. In addition to the June Gallup Rating, various other noneconomic factors such as the performance of the contesting political parties in the midterm elections, Campaign spending by the contesting parties and scandals of the Incumbent President will also play a significant role in determining the result of the forthcoming United States Presidential Election. The paper explores the influence of all the aforementioned economic and noneconomic factors on the voting behaviour of the voters in the forthcoming United States Presidential Election. The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in the 2020 US presidential election. This means that the incumbent party is most likely to lose the upcoming election.
Item Type:  MPRA Paper 

Original Title:  Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression 
English Title:  Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression 
Language:  English 
Keywords:  US Presidential Election, Machine Learning, Lasso Regression, Economic Factors, None Economic Factor, Forecasting, Prediction 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C10  General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs 
Item ID:  103889 
Depositing User:  Pankaj Sinha 
Date Deposited:  03 Nov 2020 17:15 
Last Modified:  03 Nov 2020 17:15 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/103889 