Paper Contents
Abstract
This research presents a Stock Price Prediction System that aims at helping investors to make appropriate financial decisions. It incorporates machine learning methods such as SVM, Random Forest, LSTM, and Deep Neural Networks to study historical stock data and predict its future. The system enhances accuracy through feature selection and data normalization pertaining to critical market indicators. It aids in managing risk, optimizing strategies, and assessing portfolio performance through short- and long-term stock movement forecasting. The model affords a dependable, systematic approach to complex financial markets by continually adapting to shifting market conditions.
Copyright
Copyright © 2025 Hamza Nadeem. This is an open access article distributed under the Creative Commons Attribution License.
