Build a Logistic Regression model

You've already built a Decision Tree model using the flights data. Now you're going to create a Logistic Regression model on the same data.

The objective is to predict whether a flight is likely to be delayed by at least 15 minutes (label 1) or not (label 0).

Although you have a variety of predictors at your disposal, you'll only use the mon, depart and duration columns for the moment. These are numerical features which can immediately be used for a Logistic Regression model. You'll need to do a little more work before you can include categorical features. Stay tuned!

The data have been split into training and testing sets and are available as flights_train and flights_test.

Import the class for creating a Logistic Regression classifier.
Create a classifier object and train it on the training data.
Make predictions for the testing data and create a confusion matrix.

Exercise

Build a Logistic Regression model

Instructions