Manually predicting house prices
You can manually calculate the predictions from the model coefficients. When making predictions in real life, it is better to use .predict()
, but doing this manually is helpful to reassure yourself that predictions aren't magic - they are simply arithmetic.
In fact, for a simple linear regression, the predicted value is just the intercept plus the slope times the explanatory variable.
$$\text{response} = \text{intercept} + \text{slope} * \text{explanatory}$$
mdl_price_vs_conv
and explanatory_data
are available.
This exercise is part of the course
Introduction to Regression with statsmodels in Python
Exercise instructions
- Get the coefficients/parameters of
mdl_price_vs_conv
, assigning tocoeffs
. - Get the intercept, which is the first element of
coeffs
, assigning tointercept
. - Get the slope, which is the second element of
coeffs
, assigning toslope
. - Manually predict
price_twd_msq
using the formula, specifying the intercept, slope, andexplanatory_data
. - Run the code to compare your manually calculated predictions to the results from
.predict()
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Get the coefficients of mdl_price_vs_conv
coeffs = ____
# Get the intercept
intercept = ____
# Get the slope
slope = ____
# Manually calculate the predictions
price_twd_msq = ____
print(price_twd_msq)
# Compare to the results from .predict()
print(price_twd_msq.assign(predictions_auto=mdl_price_vs_conv.predict(explanatory_data)))