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Elections form the foundation of democratic governance, and the ability to analyze electoral data efficiently is crucial for ensuring transparency, accountability, and informed decision-making. Traditional approaches to election analysis rely heavily on manual data collection and simple statistical summaries, which often limit scalability, accuracy, and predictive capabilities. This project proposes a data science–driven framework to automate the analysis of historical and real-time election datasets. The methodology includes data preprocessing using Python libraries such as Pandas and NumPy, exploratory data analysis (EDA) to uncover hidden patterns, and predictive modeling with machine learning algorithms including Logistic Regression, Decision Tree, and Random Forest. An interactive web application developed with Streamlit integrates visualization tools like Matplotlib, Seaborn, and Plotly, allowing stakeholders to explore insights dynamically. The system is designed for scalability and real-time adaptability, enabling predictions of election outcomes with measurable accuracy and improving public accessibility to electoral insights. This research highlights the transformative potential of data science in modern democratic processes by enhancing transparency, reducing biases in analysis, and providing stakeholders with reliable decision-support tools.