Empowering Democracy: A Comprehensive Analysis and Predictive Modelling of Voter Turnout in Indian General Elections

Abstract

This study aims to break down the complex dynamics driving voter turnout in Indian general elections, providing a detailed examination of the various elements that influence voters to engage in the democratic process. It investigates how voter engagement is changing over years, looking at socio-economic factors, regional differences and historical patterns that have an impact on civic engagement. The research utilizes a detailed exploratory data analysis to examine voter data from 1952 to 2019. Key factors influencing voting turnout are identified through statistical methods and visualizations. In order to predict and comprehend voter behaviour based on various socio-demographic parameters, the study uses advanced machine learning algorithms, such as Random Forest, XGBoost, LSTM and other important models. This project contributes to the understanding of voter behaviour, providing actionable insights for improving democratic participation in the Indian electoral landscape by utilizing hyper-localized constituency-wise data. Previous studies mostly looked into the political landscape of other countries and did not use any hyper-localized data. The study reveals regional differences, socio-economic linkages and important drivers of voter turnout. It highlights the value of focused campaigns, interventions tailored to a certain region and the use of technology to increase political engagements.