import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_precision_recall_curve

X, y = fetch_openml(
    name="blood-transfusion-service-center",
    as_frame=True, return_X_y=True,
)
# Make columns and classes more human-readable
X.columns = ["Recency", "Frequency", "Monetary", "Time"]
y = y.apply(
    lambda x: "donated" if x == "2" else "not donated"
).astype("category")

X_train, X_test, y_train, y_test = train_test_split(
    X, y, shuffle=True, random_state=0, test_size=0.5
)

classifier = LogisticRegression().fit(X_train, y_train)
plot_precision_recall_curve(classifier, X_test, y_test)