Stock Price Prediction using LSTM
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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If you find this project useful, please give it a star to show your support and help others discover it!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/AIcandyLSTMStock_iiyiedys.gitInstall Dependencies
Before running the scripts, you need to install the required libraries. You can do this using pip:pip install -r requirements.txtTraining the Model
To train the model and predict the price for the next day, use the following command:
python AIcandyLSTMtrainemabpupv.py --datafile historyprice.csv --numepochs 100 --batchsize 32 --checkpointpath aicandylstmcheckpoint_xpisdedn.pthMore 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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