The rapid growth of digital financial markets has produced large amounts of stock-related data that need efficient analysis for meaningful interpretation. Manually observing market trends takes a lot of time and is often unreliable for decision-making. This study introduces a web-based stock market prediction system that combines data analysis techniques with simple machine learning models to help users understand stock behavior. The proposed system gathers historical stock data, processes relevant indicators, and generates short-term trend predictions. A web interface is created to display stock performance through interactive charts and tables, allowing users to interpret patterns without needing technical knowledge. The system combines Python for backend processing with web technologies for real-time user interaction. Experimental results show that the system effectively identifies stock movement trends and offers an easy-to-use platform for market analysis. This approach focuses on simplicity, interpretability, and educational usability, making it suitable for both academic and practical uses in financial data analysis.
Stock Prediction, Machine Learning, Web Application, Data Analysis, Financial Forecasting
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