import pandas as pd

from sklearn.svm import SVC

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.metrics import confusion_matrix

# 1. 导入数据

data = pd.read_csv('wine.csv')

X = data.iloc[:, 1:] # 特征矩阵（去除第一列类别列）

y = data.iloc[:, 0] # 目标变量

# 2. 数据标准化

scaler = StandardScaler()

X_scaled = scaler.______________(X)

# 3. 划分训练集和测试集（测试集占比30%）

X_train, X_test, y_train, y_test = train_test_split(

X_scaled, y, test_size=0.3, random_state=50

)

# 4. 构建SVM模型（使用线性核函数，C=1.0）

svm = SVC(kernel='linear', C=1.0, random_state=50)

svm.fit(______________, y_train)

# 5. 模型预测与评估

y_pred = svm.predict(______________)

cm = confusion_matrix(y_test, y_pred)

print("混淆矩阵：")

print(cm)

# 6. 计算模型准确率

accuracy = svm.score(X_test, y_test)

print(f"模型准确率：{accuracy:.2f}")

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