/NASDAQ-Airflow-forked

Used Apache-Airflow to schedule NASDAQ-data-analysis on specified intervals.

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

NASDAQ Prediction with LSTM

This repository contains a Jupyter notebook and Python scripts that use an LSTM neural network to predict future values of the NASDAQ index based on historical data.

Files

  • NASDAQ-data-analysis-LSTM.ipynb : A Jupyter notebook that contains the data analysis, model training, and prediction code. The notebook includes detailed explanations and visualizations of the data analysis and model training process.

  • training.py : A Python script that loads the NASDAQ dataset, trains the LSTM model, and saves the trained model to a file.

  • evaluation.py : A Python script that loads the trained model, evaluates its performance on the test data, and generates a visualization of the predicted vs. actual values.

  • dag.py : A Python script that defines an Airflow DAG for running the training and evaluation tasks.

Requirements

The following packages are required to run the code in this repository:

  • Python 3.6+
  • TensorFlow 2.0+
  • Pandas
  • Numpy
  • Matplotlib
  • Scikit-learn
  • Airflow (for running the DAG)

Contributing

Contributions to this repository are welcome. If you find a bug or have a suggestion for improvement, please open an issue or submit a pull request.