Machine Learning for Absolute Beginners — Datasets
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Book 1 of the Data Science Foundations Series

This folder contains the datasets used throughout this book, exported as
plain CSV files so you can use them directly without needing scikit-learn
installed, or simply to inspect them in a spreadsheet.

REAL DATASETS (built into scikit-learn, exported here as CSV):
- breast_cancer.csv   — sklearn.datasets.load_breast_cancer() (569 rows, binary classification)
- wine.csv            — sklearn.datasets.load_wine() (178 rows, 3-class classification)
- diabetes.csv        — sklearn.datasets.load_diabetes() (442 rows, regression)
- iris.csv            — sklearn.datasets.load_iris() (150 rows, 3-class classification)

SYNTHETIC DATASETS (generated by code shown in the book, seed=42 for exact
reproducibility — re-running the book's own code will produce identical data):
- customer_segmentation_synthetic.csv  — Chapter 12 (K-Means clustering example)
- synthetic_blobs_clustering.csv       — Chapter 12 (make_blobs, illustrative clustering)
- synthetic_moons_clustering.csv       — Chapter 12 (make_moons, K-Means failure case)
- capstone_used_car_prices.csv         — Chapter 16 (final end-to-end project, 800 rows)

Note: Several additional chapters use small, inline synthetic examples (a
handful of rows defined directly in the code) that are not substantial
datasets in their own right and are not included here separately — the code
to reproduce them is shown directly in the relevant chapter.

More info and the latest version of this collection: www.waskeypress.com
