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