Project Abstract

Recently, the financial market has become increasingly complex, generating vast amounts of data flood in and out, making it challenging for analysts and potential investors to filter out important information from all the noise. A machine-learning-based stock trade prediction system is created in this study to assist users of all experience levels in making informed decisions. The project will look into the effectiveness of using a combination of advanced models, such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) hybrid and Random Forest classifier. It will investigate how combining the results from these models can lead to more accurate and reliable predictions. The results found that combining machine learning model results significantly improves prediction accuracy. This project demonstrates that integrating machine learning can enhance the reliability of stock market forecasts, providing traders with a more accurate support tool.

Keywords: Machine Learning Stock Predictor, AI Stock Market Tool, Flask Stock Predictor

 Conference Details

Session: A

Location: Sir Stanley Clarke Auditorium at 11:00 13:00

Markers: Cécilia Pradic, Gary Tam

Course: BSc Computer Science and Artificial Intelligence

Future Plans: I’m looking for work