Intelligent Visualization and Forecasting of Stocks Using Machine Learning

Accurate stock price prediction and analysis are now essential for businesses and investors in today's market. The accuracy with which traditional approaches to forecasting and visualizing stock data may predict future patterns has been demonstrated to be limited. This work suggests a novel method for utilizing machine learning algorithms for intelligent stock price and predictions and visualization in order to overcome this difficulty. The principal objective of this research is to develop a prediction model that leverages machine learning techniques to effectively assess historical stock data and generate accurate forecasts. The proposed model combines two machine learning techniques, regression, and time series analysis, to capture the complex relationships and patterns observed in the data. Additionally, the development of intelligent visualization approaches that improve stock data interpretation and comprehension is a focus of this work. The model uses cutting-edge data visualization techniques to give user engaging and clear visual representation of stock trends, which facilitates pattern recognition and helps users make well-informed investing decisions. The effectiveness proposed methodology is evaluated by employing real stock market data. The results outperform traditional forecasting methods in terms of performance. Users are additionally enabled by the intelligent visualization capabilities to comprehend the variable effecting stock value and predict future changes in the market.