Machine Learning-Based Stock Forecasting

Ensuring the accuracy of stock price predictions is crucial for maintaining financial stability and making informed decisions. The prompt identification of stock market trends and the factors that affect them is essential for developing effective investment strategies and promoting economic growth. In this paper, we introduce an innovative system that employs machine learning algorithms for real-time stock price forecasting, along with timely insights for investors. Utilizing advanced predictive models implemented through Python libraries such as TensorFlow and scikit-learn, our system analyzes historical stock data to forecast future trends, enabling proactive investment choices. By implementing this system in portfolio management, we equip investors to quickly adjust to marketchanges, thus enhancing financial stability and investment efficiency. The integration of machine learning techniques into financial decisionmaking offers a higher level of accuracy and flexibility in predicting stock prices. This helps mitigate risks, improve risk management, and ensures better returns for investors, contributing to sustainable economic growth. Through this approach, financial systems can achieve greater resilience, stability, and long-term success Indexed Terms: Machine Learning, Stock Forecasting, TensorFlow, Financial Stability.