Python Machine Learning Cookbook(Second Edition)
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The Realm of Supervised Learning

In this chapter, we will cover the following recipes:

  • Array creation in Python
  • Data preprocessing using mean removal
  • Data scaling
  • Normalization
  • Binarization
  • One-hot encoding
  • Label encoding
  • Building a linear regressor
  • Computing regression accuracy
  • Achieving model persistence
  • Building a ridge regressor
  • Building a polynomial regressor
  • Estimating housing prices
  • Computing the relative importance of features
  • Estimating bicycle demand distribution