Note: The notebook used in this repo is part of a solution tutorial Build, deploy, test and monitor a predictive machine learning model and used for demonstrating IBM Watson OpenScale only.
The notebook utilizes watson-machine-learning-client package in order to save, deploy, and score a predictive model.
Some familiarity with Python is helpful. This notebook uses Python 3.6, scikit-learn, and the Watson Machine Learning (WML) API client (watson-machine-learning-client).
You will use the sample iris flower data set.The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository. This small dataset is often used for testing out machine learning algorithms and visualizations. The aim is to classify Iris flowers among three species (Setosa, Versicolor or Virginica) from measurements of length and width of sepals and petals. The iris data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
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