What this resource explores
An introduction to statistical learning supported by Python applications. It moves from regression and classification to model assessment, tree-based methods and unsupervised learning.
- Regression and classification foundations
- Resampling and model selection
- Applied examples using Python
Who it is for
Readers exploring practical data analysis and the foundations of predictive models.
Background & preparation
Comfort with quantitative examples helps. The book includes Python-based labs; programming familiarity makes it easier to connect methods with code.
A way to approach it
Compare regression and classification first, then use model assessment to interpret later methods. Keep the Python examples beside the methodological discussion.
Edition & context
This is the 2023 Python edition, distinct from editions using R. Examples do not establish that a model is suitable for a particular business dataset.