Real Time Stock Market Prediction and Analysis Based on Machine Learning

Abstract: The stock market is characterized by its dynamic and volatile nature, influenced by a multitude of factors including economic indicators, political events, and market sentiment. This paper presents a real-time stock market prediction and analysis framework using machine learning techniques. Historical stock prices, technical indicators, and sentiment analysis are integrated to forecast short-term market movements. We explore and compare multiple machine learning models such as Linear Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Among these, LSTM demonstrates superior performance due to its ability to capture temporal dependencies in sequential data. The system utilizes real-time data feeds from financial APIs and displays actionable insights through a user-friendly dashboard. Our findings indicate that machine learning, especially deep learning models, significantly enhance the accuracy of stock market predictions and support informed decision-making.