A machine learning approach to predict young voter enthusiasm based on non-political factors
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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Brac University
2021
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10361-144632022-01-26T10:21:53Z A machine learning approach to predict young voter enthusiasm based on non-political factors Rahman, Md. Nowroz Junaed Pantho, Md. Humaun Kabir Fuad, Nafis Majumdar, Mahbubul Alam Shoumo, Syed Zamil Hasan Department of Computer Science and Engineering, Brac University Machine Learning Voting Young Voter Turnout Random Forest Support Vector Machine XGBoost Naive Bayes Non-political This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-68). The right to vote is considered to be the backbone of democracy. As we are entering the third decade of 21st century more and more countries around the world are adopting the democratic government system. One of most important element of democratic country is the power vested in the common people and one of the way the people are expected to exercise this power is to elect a qualified candidate to lead their country. The only way to make this election process effective is to make sure everybody participates in the process. The people who are eligible to participate in this election process to elect a candidate are called ”voters”. A substantial amount of these voters are young voter or voters who have newly been registered. It has been noticed that young voters in most of the countries are reluctant to participate in the voting process. There are many social, psychological and other non-political factors behind this reluctance. This research seeks to find those factors that motivates or repels a young voter to participate in the voting process. Besides finding the factors this research will also try to determine whether a young voter is likely to vote in an election or not based on those factors mentioned before. The data set of this research was prepared by surveying via Google Forms. Later the data set was analyzed to find out the reasons behind their participation. RFECV was used to select the optimum features and later Support Vector Machine, Random Forest, Extreme Gradient Boosting and Naive Bayes were used to predict voter participation based on the set of optimum features. In such an experimental setup Extreme Gradient Boosting and Support Vector Machine with a Gaussian kernel has shown more promising results than the other aforementioned models. The aim of this research is to predict whether a young voter will participate in the voting process or not and find the reasons behind it so, that maximum voter turnout can be ensured and perfect democracy can be achieved. Md. Nowroz Junaed Rahman Md. Humaun Kabir Pantho Nafis Fuad B. Computer Science 2021-06-01T17:46:37Z 2021-06-01T17:46:37Z 2020 2020-04 Thesis ID: 16101158 ID: 16101156 ID: 16101267 http://hdl.handle.net/10361/14463 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 70 pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
| language |
en_US |
| topic |
Machine Learning Voting Young Voter Turnout Random Forest Support Vector Machine XGBoost Naive Bayes Non-political |
| spellingShingle |
Machine Learning Voting Young Voter Turnout Random Forest Support Vector Machine XGBoost Naive Bayes Non-political Rahman, Md. Nowroz Junaed Pantho, Md. Humaun Kabir Fuad, Nafis A machine learning approach to predict young voter enthusiasm based on non-political factors |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Majumdar, Mahbubul Alam |
| author_facet |
Majumdar, Mahbubul Alam Rahman, Md. Nowroz Junaed Pantho, Md. Humaun Kabir Fuad, Nafis |
| format |
Thesis |
| author |
Rahman, Md. Nowroz Junaed Pantho, Md. Humaun Kabir Fuad, Nafis |
| author_sort |
Rahman, Md. Nowroz Junaed |
| title |
A machine learning approach to predict young voter enthusiasm based on non-political factors |
| title_short |
A machine learning approach to predict young voter enthusiasm based on non-political factors |
| title_full |
A machine learning approach to predict young voter enthusiasm based on non-political factors |
| title_fullStr |
A machine learning approach to predict young voter enthusiasm based on non-political factors |
| title_full_unstemmed |
A machine learning approach to predict young voter enthusiasm based on non-political factors |
| title_sort |
machine learning approach to predict young voter enthusiasm based on non-political factors |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14463 |
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