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README.md

Watson Studio on Cloud Pak for Data sample notebooks

With the IBM Watson Studio Notebook editor, you can create Python, Scala, and R notebooks to analyze your data. For more information see https://www.ibm.com/support/knowledgecenter/SSQNUZ_2.5.0/wsj/analyze-data/notebooks-parent.html.

Run the sample notebooks to learn new techniques or to use as templates for your own notebooks.

Save compress and deploy a Keras model
This notebook demonstrates how to train, deploy, and score a Deep Learning model using the Watson Machine Learning. Specifically, it shows you how to save the trained Deep Learning model as a .tgz file locally and deploy it online.

Use PMML to predict species of irises
This notebook demonstrates how to store a sample Predictive Model Markup Language (PMML) model and score test data. You will use the Iris data set to predict the species of an iris flower. This data set contains measurements of the iris perianth flower.

Use the Python function feature to scrape a webpage
This notebook demonstrates how to save, deploy, and score a Python function. Although the score() function is intended to score a Python function, it has other custom functionality such as preprocessing texts.

Use scikit-learn to predict hand-written digits
This notebook demonstrates how to utilize the watson-machine-learning-client package in order to save, deploy, and score a predictive model.

Use scikit-learn to predict the price of a car
This notebook gives an insight on how to handle regression problems in data science. In machine learning, regression is used when the target (the variable that will be predicted) is numerical. There are various regression algorithms that can be used to predict numerical data and you will compare the results of three of them.

Use scikit-learn to predict the price of a car (Batch)
This notebook gives an insight on how to handle regression problems in data science. In machine learning, regression is used when the target (the variable that will be predicted) is numerical. There are various regression algorithms that can be used to predict numerical data and you will compare the results of three of them.

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