Advertising Optimization Module

Businesses may find online advertising complex and confusing. One challenge is to maximize results in a cost-effective manner. Based on historical data from advertising campaigns, we developed a response model to simulate different types of custom audience behavior.

Combining this with a model for campaign parameter selection, we created a virtual environment to run advertising campaign simulations.


  • Reinforcement Learning
  • Recurrent Neural Network
  • Scipy
  • PyTorch
  • Random Forest
  • Decision Tree
  • Scikit-learn
  • PostgreSQL
  • Python
  • Numpy
  • Jupyter
  • Docker
  • Kubernetes

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