import evalml
from evalml.preprocessing import load_data
[docs]def load_churn(n_rows=None, verbose=True):
"""Load credit card fraud dataset.
The fraud dataset can be used for binary classification problems.
Arguments:
n_rows (int): Number of rows from the dataset to return
verbose (bool): Whether to print information about features and labels
Returns:
(pd.Dataframe, pd.Series): X and y
"""
churn_data_path = (
"https://api.featurelabs.com/datasets/churn.csv?library=evalml&version="
+ evalml.__version__
)
return load_data(
path=churn_data_path,
index="customerID",
target="Churn",
n_rows=n_rows,
verbose=verbose,
)