Stock market prediction is a highly complex and challenging problem due to the dynamic, volatile, and non-linear nature of financial markets. Stock prices are influenced by a wide range of factors including economic indicators, company performance, global events, investor psychology, and sudden market fluctuations, making accurate forecasting extremely difficult. Despite these challenges, reliable stock trend prediction plays a crucial role in modern financial systems, as it assists investors, traders, and financial institutions in making informed investment decisions, optimizing portfolio management, and minimizing financial risks.

This research paper presents a comprehensive stock trend prediction system that integrates machine learning techniques with technical analysis indicators to improve forecasting accuracy. Real-time historical stock data of selected National Stock Exchange (NSE) listed companies is collected dynamically using the Yahoo Finance Application Programming Interface (API). The dataset consists of essential financial attributes such as opening price, closing price, highest price, lowest price, and trading volume, which are widely used in financial time-series analysis.

To capture different patterns in stock price movements, multiple machine learning models are implemented and evaluated, including Linear Regression, Polynomial Regression, and Support Vector Regression (SVR). Linear Regression is utilized to model basic linear trends, while Polynomial Regression is applied to capture non-linear relationships within the data. Support Vector Regression, using a radial basis function kernel, is employed to handle complex and highly volatile stock price behavior more effectively. In addition to predictive modeling, technical indicators such as short-term, medium-term, and long-term moving averages are calculated to analyze market trends and generate buy and sell signals based on crossover strategies.

Furthermore, an interactive web-based dashboard is developed using the Dash framework and Plotly visualization library to provide an intuitive and user-friendly interface for stock analysis. The dashboard enables users to select different stocks, date ranges, prediction models, and technical indicators, while visualizing historical price movements, trading volume patterns, trend signals, and future price forecasts in real time. The system also offers downloadable visual reports in multiple formats, enhancing usability for analysis and documentation purposes.

Experimental results demonstrate that Support Vector Regression outperforms Linear and Polynomial Regression models in predicting non-linear stock price trends, providing smoother and more realistic future forecasts. The proposed system effectively combines data-driven machine learning techniques with traditional technical analysis, making it a robust and scalable decision-support tool for stock market analysis. This approach highlights the potential of machine learning in financial forecasting and provides a strong foundation for future enhancements such as deep learning models, sentiment analysis, and real-time automated trading strategies.