Online Voting System Using Machine Learning and Blockchain

Abstract: The Online Voting System using Blockchain with Ethereum and Machine Learning is a decentralized and secure digital voting platform aimed at ensuring transparency and integrity in elections. By utilizing blockchain technology, specifically Ethereum with Ganache, this system guarantees immutable storage of votes, eliminating any possibility of manipulation. To enhance voter authentication, the system incorporates a face recognition module, which verifies a voter’s identity before allowing them to cast their vote. The voter registration process is managed by an administrator, who can add multiple voters through bulk data uploads, including images. Election and candidate management are also handled by the administrator, ensuring structured election processes. Once a voter casts their vote, it is permanently recorded on the Ethereum blockchain, preventing any unauthorized alterations. The system enforces a strict one-vote-per-voter rule, ensuring a fair electoral process. The election results are securely displayed after voting concludes, providing an unbiased outcome. Additionally, machine learning algorithms such as Decision Tree, Random Forest, and Logistic Regression are integrated to predict future election trends. These models analyze historical election data based on multiple factors, including candidate demographics, financial assets, liabilities, and voter behavior, providing insightful forecasts. The proposed system employs Python with Django for backend development, while the frontend is built using HTML, CSS, JavaScript, and Bootstrap. By combining blockchain technology for secure voting, face recognition for fraud prevention, and machine learning for predictive analytics, this system enhances trust in digital elections, promoting a fair and transparent democratic process.

Keywords: Face recognition, Blockchain, Django, Web development, Machine Learning