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By Vinodh Mohan, Rich Hagarty, Ying Chen | Published November 5, 2018 - Updated November 5, 2018
AnalyticsArtificial IntelligenceData ScienceMachine LearningPython
This code pattern demonstrates how data scientists can leverage IBM Watson Studio Local to automate the building and training of a machine learning model to classify wines. It applies Principal Component Analysis (PCA) on a wine dataset to extract features. These components are then used to create a classification model that predicts wine categories.
Using the IBM Watson Studio Local suite of tools, this code pattern provides an example data science workflow which attempts to classify wine into three categories based on their chemical properties.
Feature engineering is used to limit the number of properties needed to classify a wine. Using Pricipal Component Analysis (PCA), two principal components are extracted from the wine dataset to build our classification model.
Our classification model will apply Logistic regression on the extracted components to predict the wine categories.
After completing this code pattern, you’ll understand how to:
Get the detailed instructions in the README file. These steps will show you how to:
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