This is an example to deploy iris model prediction.

The notebook section has the jupyter notebooks and the converted code to python.

Steps identified:

1. Define Problem.
2. Prepare Data.
3. Evaluate Algorithms.
4. Improve Results.
5. Save the model.
6. Predict on incoming set of new data.

The code also explain example to send data from html to Flask server to predict on new data points.

The Model is generate and deploy on aws lambda using zappa

To read more about: https://www.linkedin.com/pulse/pushing-jupyter-notebooks-production-teja-srivastasa/