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
Exercise instructions
- Using
taiwan_real_estate
, create a scatter plot ofsqrt_dist_to_mrt_m
versusn_convenience
, colored byprice_twd_msq
. - Create an additional scatter plot of
prediction_data
, without a legend, and withmarker
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()