SVM代码无法识别日期时间列

import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 假设你有一个包含日期时间列的数据集
data = {
    'datetime': ['2023-01-01 12:00:00', '2023-01-02 13:00:00', '2023-01-03 14:00:00'],
    'label': [0, 1, 0]
}
df = pd.DataFrame(data)

# 解析日期时间列
df['datetime'] = pd.to_datetime(df['datetime'])

# 提取特征
df['year'] = df['datetime'].dt.year
df['month'] = df['datetime'].dt.month
df['day'] = df['datetime'].dt.day
df['hour'] = df['datetime'].dt.hour

# 删除原始的日期时间列
df = df.drop(columns=['datetime'])

# 准备数据
X = df[['year', 'month', 'day', 'hour']]
y = df['label']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练SVM模型
model = SVC()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估模型
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')