A Comparative Analysis of Machine Learning and Deep Learning in Exploring Educational Inequality

Educational inequality poses a notable challenge in India, influenced by factors such as household income, parental education, school quality, and availability of technology.This study examines the differences between machine learning (ML) and deep learning (DL) models when analyzing the factors linked to educational inequality, utilizing data encompassing household and schoolrelated elements.This research evaluates the predictive capabilities of each model in forecasting educational outcomes by focusing on accuracy, interpretability, and computational efficiency.The results show that both types of models offer useful insights, each presenting unique trade-offs between interpretability and predictive accuracy.