Real-Time Stock Price Prediction and Visualization Using LSTM and Matplotlib

In this paper, we present a big data-based stock price prediction method to analyze market trends better. Investors and market analysts need to predict correctly as the stock market is getting more complex, and volatile. To solve this problem, we use historical stock data that has been prepared and processed with Apache Spark in conjunction with real-time signals collected from the Alpha Vantage API. There are two main types of information available from stock data: financial and technical. In this proposed work, we have tried to create a predictive time series model using Long Short-Term Memory (LSTM) neural network. The model is then tested based on some key metrics i.e., Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), to illustrate how well the model performs in predicting upcoming stock prices. Another key aspect the paper focuses on is how big data can be integrated with machine learning to analyze massive datasets such that runtime effectively decreases. In other words, this is a wholesale service for stock market trend analysis and institutional-level investment decisions. The results of this experiment suggest that the prediction accuracy of stock prices based on machine learning models, specifically LSTM can greatly improve market analysis.