Stock Price Prediction with LSTM

This project utilizes a Long Short-Term Memory (LSTM) neural network to predict stock prices based on historical data. LSTMs are a type of Recurrent Neural Network (RNN) that are well-suited for time series forecasting due to their ability to retain long-term dependencies in sequential data. By training on past stock prices, the model learns patterns and trends to make future predictions. The goal of this project is to provide a basic implementation of LSTM for stock price forecasting, showcasing its potential in financial market analysis.

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Getting Started

Clone the Repository

To get started with this project, clone the repository using the following command:

git clone https://github.com/TruongNV-hut/AIcandy_LSTM_Stock_iiyiedys.git

Install Dependencies

Before running the scripts, you need to install the required libraries. You can do this using pip:

pip install -r requirements.txt

Training the Model

To train the model and predict the price for the next day, use the following command:

python AIcandy_LSTM_train_emabpupv.py --data_file history_price.csv --num_epochs 100 --batch_size 32 --checkpoint_path aicandy_lstm_checkpoint_xpisdedn.pth

More Information

To learn more about this project, see here.

To learn more about knowledge and real-world projects on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), visit the website aicandy.vn.

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