Enhancing Stock Price Forecasting Accuracy with Hybrid Machine Learning Models and Interactive Visualization: A Dash-Based Approach Integrating Time Series Analysis

Predicting the future of stock market is essential for financial professionals and investors. This paper proposes a reliable method for better accuracy in stock price prediction by using the combination of interactive visualization tools and machine learning algorithms. The proposed framework majorly depends on Time Series analysis, which is used to identify previous trends and patterns in stock prices. Random Forest are suitable for financial data, which frequently displays nonlinear correlations, because to its capacity to handle high attribute data and it is very efficient in determining the best possible decision boundary. Random Forest will provide accurate predictions