1. Learn
    2. /
  2. Courses
    3. /
  3. Introduction to Predictive Analytics in Python

Exercise

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.

Instructions

100 XP
  • 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.