Predict future stock prices using LSTM neural networks. This beginner-friendly project analyzes past stock data to forecast future trends, helping users make informed investment decisions.
Python: The primary programming language for writing the script and implementing the LSTM model.
Pandas: A Python library for data manipulation and analysis. It's commonly used for handling the historical stock price data.
NumPy: A fundamental package for numerical computing in Python. It's used for array manipulation and mathematical operations, often in conjunction with Pandas.
Matplotlib: A plotting library for creating visualizations in Python. It's used for plotting the actual and predicted stock prices.
Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow. It's used for building and training the LSTM model.
scikit-learn (optional): If you're performing additional data preprocessing or evaluation tasks beyond what Keras provides, you might use scikit-learn, a machine learning library in Python.
To run the LSTM stock price prediction script, follow these steps:
- Clone the repository
- Change directory to the repository using - cd LSTM-Stock-Price-Prediction
- Install dependencies using- pip install -r requirements.txt
- Run the python script in Jupyter Lab or Notebook
