A machine learning perspective in predicting Historical Index Data

Predicting the stock market is a perpetual challenge due to the vast amount of data generated daily. This study explores the application of machine learning (ML) techniques to address this challenge. With the aid of big data analytics, we investigate the advancements in ML for stock market forecast. The primary focus of this study is the prediction of Historical Index Data – Nifty, with implications that extend to other stocks. Through an extensive literature review, we examine existing research on stock market prediction and identify gaps in the current understanding. Through systematic experiments and rigorous evaluation, we contribute to the existing body of knowledge on stock market prediction. Our findings highlight the potential of ML techniques, particularly the hybrid XGBoost-GRU model, for accurate and informed stock market forecasting.