Stock price prediction using LSTM and GRU

This project involved the implementation of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning models for predicting stock prices in the foreign exchange market. The primary focus was on leveraging the power of recurrent neural networks to capture temporal patterns and relationships in financial time series data. By utilizing advanced machine learning techniques, the project aimed to enhance the accuracy and reliability of stock price forecasting, which could benefit traders and investors alike.