Education Literacy Rate Forecasting Using Ensemble Models

Abstract

This abstract introduces a novel approach to forecasting the literacy rate in Indian education using ensemble models. This research proposes a methodology that combines the strengths of multiple algorithms, including decision trees, random forests, gradient boosting, and neural networks, to create an ensemble model capable of providing accurate predictions. By utilizing historical data on literacy rates, socio-economic factors, government policies, and educational initiatives, the proposed model aims to offer insights into future literacy trends in India. The study employs advanced machine learning techniques to analyze and interpret complex data patterns, contributing to a deeper under-standing of the factors influencing literacy rates and informing targeted interventions for educational development. The research revealed that linear regression had higher performance, achieving a R² score of 0.9635, which indicates a robust connection between the anticipated and actual values.