STOCK MARKET PREDICTION

Author(s)	Mr. Thilak B, Mr. Syed Zain, Dr. Shalini R
Country	India
Abstract	The stock market is a highly dynamic environment where anticipating price movements is both challenging and valuable for investors, traders, and financial analysts. This project introduces a stock price forecasting system built on Long Short-Term Memory (LSTM) networks, implemented using Python and deployed with Streamlit. Historical stock data obtained from Yahoo Finance is utilized to train the model, enabling the prediction of future price trends. The application provides an interactive platform where users can enter stock tickers, select date ranges, and configure model parameters with ease. Model accuracy is assessed through error metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The system also generates visualizations of historical trends, prediction outputs, and forward projections to enhance user interpretation. The project highlights the effectiveness of deep learning approaches in financial time-series forecasting while offering an accessible, user-friendly interface for stock analysis
Keywords	Stock Forecasting, LSTM, Deep Learning, Time-Series Analysis, Streamlit, Financial Prediction
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-06
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.59625

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research