Making predictions
Once your model is ready, you can use it to make predictions for a campaign. It is important to always use the latest information to make predictions.
In this exercise you will, given a fitted logistic regression model, learn how to make predictions for a new, updated basetable.
The logistic regression model that you built in the previous exercises has been added and fitted for you in logreg
.
This exercise is part of the course
Introduction to Predictive Analytics in Python
Exercise instructions
- The latest data is in
current_data
. Create a data framenew_data
that selects the relevant columns fromcurrent_data
. - Assign to
predictions
the predictions for the observations innew_data
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit a logistic regression model
from sklearn import linear_model
X = basetable[["age","gender_F","time_since_last_gift"]]
y = basetable[["target"]]
logreg = linear_model.LogisticRegression()
logreg.fit(X, y)
# Create a DataFrame new_data from current_data that has only the relevant predictors
new_data = ____[[____, ____, ____]]
# Make a prediction for each observation in new_data and assign it to predictions
predictions = ____.____(____)
print(predictions[0:5])