Getting Started
Machine learning fits a rule from examples instead of having every decision written by hand. Begin with a clear prediction target and a simple baseline.
Understand the idea
Features are inputs available at prediction time; the target is what you want to estimate. Training finds a rule using labelled examples. Evaluation must use data not used to fit or choose that rule. This tiny constant baseline demonstrates the idea of predicting before adding complex models.
Where to try it
Open this example with Try in playground. The code loads as editable text and does not run until you select Run. The first run downloads Python. Each run starts fresh; temporary files created by a script are not files on your PC.
A worked example
from statistics import mean
training_targets = [10, 12, 14]
prediction = mean(training_targets)
print(prediction)
Read the result
The baseline predicts 12 for any new case. It ignores features, which makes it simple to beat and easy to explain.
Try one change
Define a target for workshop demand and list which inputs would genuinely be known before bookings close.
For package options, see the scikit-learn documentation.