Классическое машинное обучение на Python — Минимальный пример классификации

import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

df = pd.read_csv("customers.csv")
X = df[["age", "orders_count", "avg_check"]]
y = df["churn"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train)
X_test_s = scaler.transform(X_test)

model = LogisticRegression(max_iter=1000)
model.fit(X_train_s, y_train)
pred = model.predict(X_test_s)

print(classification_report(y_test, pred))