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Predictive Analytics For Dummies by Tommy Jung, Mohamed Chaouchi, Anasse Bari

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Chapter 13

Creating Basic Examples of Unsupervised Predictions

In This Chapter

arrow Working with a sample dataset

arrow Creating simple predictive models using clustering algorithms

arrow Visualizing and evaluating your results

This chapter is about creating a couple of simple predictive models using unsupervised learning with clustering algorithms such as K-means and DBSCAN. These examples use the Python programming language, version 2.7.4, on a Windows machine. Please refer to Chapter 12 if you need instructions on installing Python and scikit-learn machine-learning package.

No prior knowledge of supervised learning is required to understand the concepts of unsupervised learning. Supervised learning is when the output categories are known; unsupervised learning is when the output categories are unknown. Chapter 12 covers examples of supervised learning with classification and regression algorithms.

You can read Chapters 12 and 13 independently. One advantage of reading both chapters in the same session is that you’ll be able to reuse the work that you did to load the Iris dataset into the Python interpreter (the command line where you enter the code statements or commands). So if you’re continuing ...

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