This repository provides a comprehensive guide and implementation of a stock price prediction model utilizing Long Short-Term Memory (LSTM) neural networks. LSTMs are a type of recurrent neural network (RNN) adept at capturing temporal dependencies in sequential data, making them particularly effective for time series forecasting tasks such as stock price prediction.
Predicting stock prices is a complex challenge due to the volatile and stochastic nature of financial markets. This project aims to leverage the capabilities of LSTM networks to model and forecast stock prices by learning from historical data. The implementation includes data preprocessing, model construction, training, evaluation, and visualization of the predicted versus actual stock prices.
- Data Preprocessing: Loading and preparing historical stock price data for modeling.
- Model Architecture: Building and training an LSTM network tailored for stock price prediction.
- Evaluation: Assessing the model's performance using appropriate metrics.
- Visualization: Plotting actual versus predicted stock prices to visualize the model's accuracy.
To run this project, ensure you have the following dependencies installed:
- Python 3.x
- TensorFlow
- Keras
- Pandas
- NumPy
- Matplotlib
- Scikit-learn
You can install the required packages using pip:
pip install tensorflow keras pandas numpy matplotlib scikit-learn
git clone https://github.com/ErenElagz/Price-Prediction-Using-LSTM-Algorithm-in-Neural-Networks.git
cd Price-Prediction-Using-LSTM-Algorithm-in-Neural-NetworksContributions are welcome! Please feel free to submit a pull request or open an issue to discuss improvements or features.
This project is licensed under the MIT License.
This project is intended for educational purposes and should not be used as financial advice. Always conduct thorough research or consult financial experts before making investment decisions.