Stock Data Prediction and Visualization using Advanced Learning and Dash Framework

The modern financial market faces the challenge of identifying an effective approach for predicting stock market prices and making informed investment decisions. The stock market is a complex, transformative system, and long-term investments are crucial. However, evaluating shares and calculating basic company values is challenging. This research work presents a comparison of machine learning-aided algorithms for predicting future stock market prices and analyzing market behavior. The method accurately analyzes supervised algorithms and compares the best ones for predicting future stock market prices. The market is influenced by factors like economics, making market analysis and forecasts crucial for informed decision-making and maximizing investment returns. The research work presents a new method for visualizing and predicting products using Dash, a Python framework for web applications. The methodology involves acquiring historical stock data from reliable sources and preprocessing it to extract relevant features. This data is then integrated into a user-friendly dashboard for interactive trend exploration and future stock price forecasting. The dashboard offers interactive charts, analyses, and correlation matrices to understand stock data structure while using advanced learning techniques like ARIMA and LSTM networks for accurate forecasts. Forecasting aids in making informed decisions and reducing risk in volatile markets. It integrates features into a user-friendly dashboard, helping investors overcome trading complexities and achieve financial objectives. However, there's a need for more predictable and efficient algorithms, making them accessible and interfaced.