TradeSense : Predictive Analytics for Stocks

The stock market is a complex and rapidly changing environment where accurate predictions can greatly enhance investment decisions. Traditional forecasting methods often struggle to adapt to fluctuating market conditions and diverse economic influences. This study presents a stock prediction and analysis system that uses machine learning and predictive analytics to identify trends and forecast stock movements more effectively. The system processes historical data through techniques such as data cleaning, feature extraction, and model training using algorithms like Linear Regression and Long Short-Term Memory (LSTM) networks. Built with Python for predictive modeling and PHP for backend integration, the platform offers real-time data visualization and performance tracking through an intuitive web interface. By merging AIpowered insights with user-friendly design, the proposed system aims to empower investors and analysts with more reliable, datadriven tools for making informed financial decisions