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
