/ba_machine_learning_2019

Bachelor Thesis 2019, Stock market prediction using deep LSTM networks (Vorhersage von Aktienkursen mittels tiefen LSTM-Netzwerken), Joel Erzinger und Silvio Jäger, NTB - University of Applied Sciences Buchs

Primary LanguagePython

Installation (Windows)

  • git clone https://github.com/silviojaeger/ba_machine_learning_2019
  • cd .\ba_machine_learning_2019
  • setup.ps1
  • Setup VSCode (Optional)
    • Open Folder ba_machine_learning_2019
    • Select Interpreter: command + shift + P execute Python: Select Interpreter select .\env\Scripts\python.exe
    • Activate env (Optional for terminal commands): .\activate.ps1

Upgrade environment

  • activate.ps1
  • update_requirements.ps1
  • python -m pip install <package>
  • Commit changes

Create a new environment

  • Copy following files to a new foder .<newEnv>:
    • setup.ps1
    • update_requirements.ps1
    • add .\<newEnv>\env to .gitignore

Install graphviz (used to visualize the keras graph)

Install CUDA library(used to train the models on your GPU)