Analyzing and Predicting Election Outcomes in Indian Politics

This project, employs data mining and machine learning techniques to unravel the complexities of electoral dynamics. It explores candidate attributes, party affiliations, and historical trends, enhancing predictive accuracy through algorithms like Decision Trees, Random Forests, SVM, and XGBoost with hyper parameter tuning(Grid Search). Noteworthy contributions include SVM optimization through grid search and a thorough comparative study of model performance. Visualizations shed light on the relationships between winning candidates and demographics. Acknowledging successes and limitations, the project navigates the challenges of model interpretability in political contexts, presenting a nuanced exploration of election prediction in Indian politics.