Research Analysis for Detecting Duplicate Entries in Electoral Voter List

Electoral voter lists form the backbone of democratic processes, ensuring that every eligible citizen is granted a fair opportunity to vote. However, large-scale voter databases often suffer from data quality issues such as duplicate or nearduplicate entries caused by spelling variations, data entry errors, migration, and inconsistent demographic updates. These redundancies can compromise the integrity of elections, increase administrative costs, and reduce public trust. This research proposes an intelligent, analytics-driven framework for detecting duplicate entries in electoral voter lists using a combination of data pre processing techniques, rule-based matching, and machine learning models. The proposed system integrates phonetic similarity, demographic attribute comparison, and supervised classification models to identify potential duplicates with high accuracy. Experimental analysis demonstrates that the hybrid approach significantly outperforms traditional exact-matching techniques, offering a scalable and reliable solution for election management bodies. The study emphasizes transparency, accuracy, and scalability while maintaining compliance with ethical and data privacy considerations.