Stocks Analysis and Prediction Using Big Data Analytics - Bhargav Sai, Gollapalli; Vikram, Dara (Journal of Neonatal Surgery, 2025)
Big data analytics plays a crucial role in various sectors, enabling the accurate prediction and analysis of large datasets. This approach focuses on stock market prediction, where large volumes of stock data are processed to predict daily gains or losses. By utilizing big data tools, such as the PySpark API, streaming or batch data is processed to generate predictions based on historical stock information. The goal is to identify patterns in stock price movements and predict future trends with a high level of accuracy. Performance is evaluated using R-squared metrics, ensuring that the most effective model is selected. Among the models tested, the Long Short-Term Memory (LSTM) algorithm demonstrates the highest predictive accuracy, achieving an R-squared value of 0.97%. This result highlights LSTM's capability to closely match predicted stock prices with actual test data, offering a reliable solution for stock market forecasting. The approach showcases the potential of big data and machine learning in financial analysis, helping investors make informed decisions based on historical trends and future predictions.