Visualizing two numeric explanatory variables

The code for visualizing two numeric explanatory variables is the same as you've seen before: create a layer of the actual data points, and add a layer of the prediction points to see how they match. In the case of two numeric explanatory variables, the prediction point layer will look like a grid.

taiwan_real_estate and prediction_data are available with the square-root transformed variable sqrt_dist_to_mrt_m.

This exercise is part of the course

Intermediate Regression with statsmodels in Python

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Exercise instructions

Using taiwan_real_estate, create a scatter plot of sqrt_dist_to_mrt_m versus n_convenience, colored by price_twd_msq.
Create an additional scatter plot of prediction_data, without a legend, and with marker set to "s" (for squares).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create scatter plot of taiwan_real_estate
____

# Create scatter plot of prediction_data without legend
____

# Show the plot
plt.show()

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