Predicting stock trends has long been a challenging task due to market volatility and complex interdependencies. This paper presents a web-based stock trend prediction system that integrates three machine learning algorithms—Lin- ear Regression (LR), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM)—to forecast the trends. By averaging the predictions of these models, our system provides a more reliable recommendation on whether to buy or avoid a stock. Users can log in or register on the platform, input stock names, and choose indicators, while a close price vs. year graph visualizes stock performance. The model's recommendations are validated using historical data from multiple companies, demonstrating the effectiveness of our approach.