Paper Contents
Abstract
Predicting Stock Market Trends Using Machine Learning is an approach aimed at addressing the challenges of forecasting in a highly volatile financial environment. This project focuses on using Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN) known for its ability to capture long-term dependencies in sequential data. By analyzing historical stock price data, the LSTM model learns complex temporal patterns to generate more reliable price predictions. The goal is to assist investors and analysts in making informed decisions by reducing uncertainty and enhancing predictive accuracy through machine learning.The implementation involves data collection from platforms like Alphavantage and Kaggle, preprocessing through normalization, and model training using Python within a Jupyter Notebook environment. Key components such as data visualization, hyperparameter tuning, and prediction evaluation are integrated into the workflow. The model demonstrates improved performance over traditional statistical methods, validating the effectiveness of deep learning in financial forecasting. Ultimately, this project highlights the potential of machine learning to revolutionize stock market analysis and support the development of intelligent trading strategies.
Copyright
Copyright © 2025 Mohammad Huzaifa, Gajjela Shirisha, Kuragayala Sreeja, Mogili Saikumar, Thaniparthi Abhishek Rao. This is an open access article distributed under the Creative Commons Attribution License.
