Stock Market Prediction Using Deep Learning

In this project, we aim to implement one of the most complex machine learning approaches to predict stock prices and forecast share values. This paper effectively employs machine learning techniques and analyzes various strategies for forecasting future stock prices. The main objective is to develop a prediction model and evaluate its accuracy by comparing it with real data. Additionally, we strive to enhance the accuracy of the model by utilizing neural networks. Specifically, we employ the Long Short-Term Memory (LSTM) model, which has proven to be highly effective in sequence prediction problems and yields more accurate results compared to previous algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). LSTM models have the ability to retain past information, which is crucial in our case, as past stock prices play a significant role in predicting future prices. By combining reinforcement learning techniques with LSTMs, we can achieve accurate predictions in the stock market, enabling us to make informed decisions on when to buy or sell stocks.