Published Paper Details:

Machine learning serves stock market prediction as an essential application through which people alongside organizations obtain data-driven investment intelligence. The system establishes stock market prediction through the implementation of machine learning approaches and deep learning techniques toward stock price forecasting. The system runs on Python while using Flask for web-based deployment which enables users to obtain real-time predictions through an effortless interface. The model acquires historical stock information from Yahoo Finance through the use of Pandas and NumPy and yfinance libraries for processing. Deep learning acceptance requires MinMaxScaler to normalize the data before deep learning processing. The system implements a Sequential model which applies Long Short-Term Memory layers and Dropout layers and Dense layers using TensorFlow Keras for effective prediction of temporal patterns. The system performs five stages as part of its operational scope which include the collection of data followed by preprocessing procedures and model development and evaluation before deployment. Additional possible enhancements to this design will integrate multi-stock forecasting together with sentiment analysis functionality and mobile device compatibility. The main mission is to establish a powerful deep learning algorithm that does stock forecast with superior precision yet the system will also showcase an easy-to-use interface and instant prediction capabilities for user-defined stocks.