Building a logistic regression model

You can build a logistic regression model using the module linear_model from sklearn. First, you create a logistic regression model using the LogisticRegression() method:

logreg = linear_model.LogisticRegression()

Next, you need to feed data to the logistic regression model, so that it can be fit. X contains the predictive variables, whereas y has the target.

X = basetable[["predictor_1","predictor_2","predictor_3"]]`
y = basetable[["target"]]
logreg.fit(X,y)

In this exercise you will build your first predictive model using three predictors.

This exercise is part of the course

Introduction to Predictive Analytics in Python

View Course

Exercise instructions

Import the methodlinear_model from sklearn.
The basetable is loaded as basetable. Note that the column "gender" has been transformed to gender_F so that it can be used as a predictor. Construct a DataFrame X that contains the predictors age, gender_F and time_since_last_gift.
Construct a DataFrame y that contains the target.
Create a logistic regression model.
Fit the logistic regression model on the given basetable.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import linear_model from sklearn.
from ____ import ____

# Create a DataFrame X that only contains the candidate predictors age, gender_F and time_since_last_gift.
X = ____[[____, ____, ____]]

# Create a DataFrame y that contains the target.
y = ____[[____]]

# Create a logistic regression model logreg and fit it to the data.
logreg = ____.____
logreg.____(____, ____)

Edit and Run Code