Fix polynomial regression plot rendering as zigzag line

[Copilot]

train_test_split shuffles data, so X_test is unordered — plt.plot(X_test, pred) connects points in array order, producing a zigzag instead of a smooth curve. Fix: generate a sorted uniform range via np.linspace for plotting.

# Before (zigzag)
plt.scatter(X_test, y_test)
plt.plot(sorted(X_test), pipeline.predict(sorted(X_test)))

# After (smooth curve)
X_range = np.linspace(X_test.min(), X_test.max(), 100).reshape(-1,1)
y_range = pipeline.predict(X_range)
plt.scatter(X_test, y_test)
plt.plot(X_range, y_range)

2-Regression/3-Linear/README.md

• Added explicit predict + metrics code block for the polynomial regression section (was previously omitted, leaving learners to infer from the linear regression pattern)
• Added np.linspace-based plotting code with a brief explanation of why direct X_test plotting fails

2-Regression/3-Linear/solution/notebook.ipynb

• Replaced sorted(X_test) workaround with the np.linspace approach consistent with the updated README

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[Copilot]

Pull request overview

This PR fixes the polynomial regression visualization in the regression lesson by avoiding plotting predictions against an unordered X_test (which can render as a zigzag), and aligns the lesson materials to use a smooth, uniformly sampled X_range for plotting.

Changes:

• Update polynomial regression plotting to use np.linspace + pipeline.predict(X_range) for a smooth curve.
• Expand the polynomial regression README section with an explicit predict/metrics snippet and an explanation of why direct X_test plotting can fail.

Show a summary per file

File	Description
2-Regression/3-Linear/README.md	Adds explicit prediction/metrics code and updates plotting guidance to use a uniform sorted range for smooth curves.
2-Regression/3-Linear/solution/notebook.ipynb	Replaces the sorted(X_test) plotting workaround with an np.linspace-based range and plots predictions over that range.

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• Files reviewed: 2/2 changed files
• Comments generated: 1