I am a student in Google Colab. ChatGPT

I am a student in Google Colab.
I already have X_test, y_test, and a trained model called model.
I also have these values in HKD: BASELINE_MAE, STARTER_TRAIN_MAE, STARTER_TEST_MAE, train_mae, test_mae.
pandas, matplotlib, and scikit-learn are already imported.
Please give Python code for ONE Colab cell that I can paste.
Comment every line in simple English.
Do not write a full .py file.

I will plot: predicted vs actual prices on the TEST set, with a y=x reference line.
because this chart shows: how closely unseen-car predictions match real sale prices and where the model makes large errors.
I will not plot: a heatmap or a price histogram.
because that would only show: correlations or the price distribution, not prediction accuracy on unseen cars.

Use the test set only for the predicted-vs-actual chart.
Do not plot the training rows.
Do not draw a heatmap.
Label both axes in HKD.
Use log scales so expensive outliers do not hide the ordinary cars.

Draw predictions against actual prices for the untouched test set only.

fig, ax = plt.subplots(figsize=(8, 6))

Show each test car as one point.

ax.scatter(y_test, model.predict(X_test), alpha=0.5, s=24, color="#157f86")

Find a positive range that includes every test price and prediction.

low = max(1, min(y_test.min(), model.predict(X_test).min()))
high = max(y_test.max(), model.predict(X_test).max())

Draw the ideal prediction line for comparison.

ax.plot([low, high], [low, high], color="#c52f35", linestyle="--", label="Perfect prediction")

Use log scales to keep both common cars and expensive outliers visible.

ax.set_xscale("log")
ax.set_yscale("log")

Label both axes in Hong Kong dollars.

ax.set_xlabel("Actual sale price (HKD, log scale)")
ax.set_ylabel("Predicted sale price (HKD, log scale)")

Explain the figure with its measured test error.

ax.set_title(f"Test-set prices vs predictions | MAE HK${test_mae:,.0f}")

Add a grid and line label for readability.

ax.grid(True, alpha=0.25)
ax.legend()

Avoid clipped labels and display the chart.

plt.tight_layout()
plt.show()