Stock Price Prediction using GRU and Dashboards

This research focuses on predicting stock prices using Gated Recurrent Units (GRUs), a type of Recurrent Neural Network (RNN) that effectively captures sequential dependencies in time series data. The model leverages historical stock data and presents results using interactive dashboards in Power BI, enhancing decision-making and interpretability for stakeholders. The study evaluates the accuracy and efficiency of GRU-based models against traditional approaches, demonstrating improved forecasting capabilities. Additionally, the research explores the impact of various hyper-parameters on model performance and compares GRU with other deep learning architectures, such as LSTMs, to determine the optimal approach for financial time series forecasting. The system's ability to detect trends, mitigate noise, and provide real-time insights makes it a valuable tool for investors and financial analysts. Furthermore, this study examines real-world applications, industry adoption, and the scalability of GRU-based predictive models in financial markets, ensuring robust performance across varying market conditions.