Обучите модель SVM с ядром 'sigmoid' и вычислите метрику accuracy.
# импортируем необходимые библиотеки и объявляем константы
import pandas as pd
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
scaler = StandardScaler()
RANDOM_STATE = 77
data = pd.read_csv('orders_seafood.csv')
X = data.drop(columns=['target', 'client_id'])
y = data['target']
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE)
X_train_scalled = scaler.fit_transform(X_train)
X_test_scalled = scaler.transform(X_test)
# объявляем классификатор и обучаем модель
clf_sigm = SVC(kernel='sigmoid')
clf_sigm.fit(X_train_scalled, y_train)
y_pred_sigm = clf_sigm.predict(X_test_scalled)
r = accuracy_score(y_test, y_pred_sigm)
print(r)Результат
0.601063829787234