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Original Article
Election Analysis Using Data Science
Shaik Zaheer Pasha1
Abdul Rahman2
1Student, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Assisant Professor, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-December 2025
Pages: 01-05
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403001
References
1. Election Commission of India, "Official Website," [Online]. Available: https://eci.gov.in. [Accessed: 23-Jun-2025].
2. R. Kumar, S. Mahajan, and A. Yadav, "A Machine Learning Approach for Predicting Election Results Using Social Media and Demographic Data," International Journal of Computer Applications, vol. 183, no. 19, pp. 20–25, 2021.
3. M. Rao and A. Patil, "Predictive Analytics in Elections Using Supervised Learning Techniques," International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 6, pp. 1336–1341, 2020.
4. Scikit-learn Developers, "scikit-learn: Machine Learning in Python," [Online]. Available: https://scikit-learn.org. [Accessed: 23-Jun-2025].
5. Streamlit Inc., "Streamlit — the fastest way to build and share data apps," [Online]. Available: https://streamlit.io. [Accessed: 23-Jun-2025].
6. Kaggle, "Election and Voter Datasets," [Online]. Available: https://www.kaggle.com. [Accessed: 23-Jun-2025].
7. J. D. Hunter et al., "Matplotlib: Visualization with Python," [Online]. Available: https://matplotlib.org. [Accessed: 23-Jun-2025]; and M. Waskom, "Seaborn: Statistical Data Visualization," [Online]. Available: https://seaborn.pydata.org. [Accessed: 23-Jun-2025].
8. A. Jain and S. Batra, "Data Science Approaches for Electoral Trend Analysis and Prediction," in Proc. Int. Conf. Machine Learning and Data Science (MLDS), IEEE, 2019.
9. C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015.
10. J. Han, J. Pei, and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed. Elsevier, 2011.
2. R. Kumar, S. Mahajan, and A. Yadav, "A Machine Learning Approach for Predicting Election Results Using Social Media and Demographic Data," International Journal of Computer Applications, vol. 183, no. 19, pp. 20–25, 2021.
3. M. Rao and A. Patil, "Predictive Analytics in Elections Using Supervised Learning Techniques," International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 6, pp. 1336–1341, 2020.
4. Scikit-learn Developers, "scikit-learn: Machine Learning in Python," [Online]. Available: https://scikit-learn.org. [Accessed: 23-Jun-2025].
5. Streamlit Inc., "Streamlit — the fastest way to build and share data apps," [Online]. Available: https://streamlit.io. [Accessed: 23-Jun-2025].
6. Kaggle, "Election and Voter Datasets," [Online]. Available: https://www.kaggle.com. [Accessed: 23-Jun-2025].
7. J. D. Hunter et al., "Matplotlib: Visualization with Python," [Online]. Available: https://matplotlib.org. [Accessed: 23-Jun-2025]; and M. Waskom, "Seaborn: Statistical Data Visualization," [Online]. Available: https://seaborn.pydata.org. [Accessed: 23-Jun-2025].
8. A. Jain and S. Batra, "Data Science Approaches for Electoral Trend Analysis and Prediction," in Proc. Int. Conf. Machine Learning and Data Science (MLDS), IEEE, 2019.
9. C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015.
10. J. Han, J. Pei, and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed. Elsevier, 2011.
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