Book Content
chapters • 12h24m total length
1. Foreseeing Variable Problems When Building ML Models
2. Imputing Missing Data
3. Encoding Categorical Variables
4. Transforming Numerical Variables
5. Performing Variable Discretisation
6. Working with Outliers
7. Deriving Features from Dates and Time Variables
8. Performing Feature Scaling
9. Applying Mathematical Computations to Features
10. Creating Features with Transactional and Time Series Data
11. Extracting Features from Text Variables
