k-Nearest Neighbors: Fit
In this exercise, you will build your first classification model using the churn_df
dataset, which has been preloaded for the remainder of the chapter.
The target, "churn"
, needs to be a single column with the same number of observations as the feature data. The feature data has already been converted into numpy
arrays.
"account_length"
and "customer_service_calls"
are treated as features because account length indicates customer loyalty, and frequent customer service calls may signal dissatisfaction, both of which can be good predictors of churn.
This exercise is part of the course
Supervised Learning with scikit-learn
Exercise instructions
- Import
KNeighborsClassifier
fromsklearn.neighbors
. - Instantiate a
KNeighborsClassifier
calledknn
with6
neighbors. - Fit the classifier to the data using the
.fit()
method.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import KNeighborsClassifier
from ____.____ import ____
y = churn_df["churn"].values
X = churn_df[["account_length", "customer_service_calls"]].values
# Create a KNN classifier with 6 neighbors
knn = ____(____=____)
# Fit the classifier to the data
knn.____(____, ____)