A Framework for Voting Behavior Prediction Using Spatial  Data

Shobhit Kumar Ravi; Shivam Chaturvedi; Neeta Rastogi; Nikhat Akhtar; Yusuf Perwej

doi:10.55524/

Authors

Shobhit Kumar Ravi B.Tech Scholar, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, India Author
Shivam Chaturvedi Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, India Author
Neeta Rastogi Professor, Department of Computer Science & Engineering, Ambalika Institute of Management and Technology, Lucknow, India Author
Nikhat Akhtar Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, India Author
Yusuf Perwej Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Spatial Analysis, Voter Turnout, Data Analytics, ARIMA Model, Recurrent Neural Network (RNN), Time Series Forecasting

Abstract

The greatest method to anticipate the future is to look at what has happened in the past. We shall present important election behavioral predictions in this paper. This study article will focus on the data offered by Present age wise voting statistics, voter demographics, votes cast, and spatial correlation among surrounding states in order to validate that a place's exit poll data. The major goals of our paper are to first encourage voting among different age groups based on projected circumstances, and then to understand the influence of a state's neighbours. Conclusively studying the entire voting scenario of previous years, which will aid in the forecast of citizens' voting behavior in the approaching years, as well as recognizing the root cause of the weaker portions and improving upon the flaws for a better future. Our main goal is to use some current voting data from a region to train and determine the major voting population in the various states of the United States based on their geographical influence. This will aid in the analysis of the current situation as well as assisting the government in creating awareness in places where it is missing.

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