Use scikit-learn and custom library to predict temperature with WML

This notebook contains steps and code to train a Scikit-Learn model that uses a custom defined transformer and use it with Watson Machine Learning service. Once the model is trained, this notebook contains steps to persist the model and custom defined transformer to Watson Machine Learning Repository, deploy and score it using Watson Machine Learning python client.

In this notebook, we use GNFUV dataset that contains mobile sensor readings data about humidity and temperature from Unmanned Surface Vehicles in a test-bed in Athens, to train a Scikit-Learn model for predicting the temperature.