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.
Diese Übung ist Teil des Kurses
Intermediate Regression with statsmodels in Python
Anleitung zur Übung
- Using
taiwan_real_estate, create a scatter plot ofsqrt_dist_to_mrt_mversusn_convenience, colored byprice_twd_msq. - Create an additional scatter plot of
prediction_data, without a legend, and withmarkerset to"s"(for squares).
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Create scatter plot of taiwan_real_estate
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# Create scatter plot of prediction_data without legend
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# Show the plot
plt.show()