Solanki, Gaurav and Jha, Bibek Kumar and Anand, Yash and Rout, Pritam Kumar and Mandal, Piyush (2025) Stock Market Price Prediction. International Journal of Innovative Science and Research Technology, 10 (4): 25apr1288. pp. 1861-1866. ISSN 2456-2165 (In Press)
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Abstract
This project is focused on creating a machine learning model to predict stock market prices by examining past data and market indicators. We apply regression and deep learning techniques to improve prediction accuracy. The main goal is to aid stock market analysis with a dashboard developed using the LSTM (Long Short-Term Memory) model. We will explain how the model functions and show how it can be used for making real-time predictions. We'll also talk about the challenges faced during its development. LSTM models are excellent for analyzing data that changes over time and for spotting long-term trends. They are especially useful for predicting time series, such as stock prices, because they can adapt to new market data rather than depending on fixed rules.
Item Type: | Article |
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Subjects: | T Technology > T Technology (General) |
Divisions: | Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
Depositing User: | Editor IJISRT Publication |
Date Deposited: | 02 May 2025 09:43 |
Last Modified: | 02 May 2025 09:43 |
URI: | https://eprint.ijisrt.org/id/eprint/666 |