ed-science / automation-repo

Machine learning and process automation

Process automation with Machine Learning.

1. invoice-automation-d1.ipynb - date is split into multiple columns
2. invoice-automation-d2.ipynb - instead of splitting date, using date difference in days
3. invoice-risk-model-local.ipynb - step by step notebook to run xgboost on premise
4. diabetes_redsamurai_db.ipynb - notebook which demonstrates how to fetch training data directly from DB, prepare train/test datasets and run training with XGBoost
5. diabetes_redsamurai_endpoint_db.ipynb - notebook which demonstrates how to use Flask to expose XGBoost ML model
6. convnet - cat vs dog image classification model built using Python code from book: https://www.manning.com/books/deep-learning-with-python (original source code from the book on GitHub: https://github.com/fchollet/deep-learning-with-python-notebooks)
7. forecast - future price forecast for iron/steel with Prophet model. Example how to save/load Prophet model and expose Flask API
8. regression - Keras/TensorFlow model with regression implementation to predict report execution time, before report request is submitted

Author: Andrejus Baranovskis, Red Samurai Consulting (http://redsamuraiconsulting.com)