消费金融反欺诈模型

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
import pandas as pd

# 加载数据
data = pd.read_csv('consumer_finance_data.csv')

# 特征选择和标签定义
features = ['age', 'income', 'credit_score', 'transaction_amount']
X = data[features]
y = data['is_fraud']

# 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)

# 创建决策树模型
clf = DecisionTreeClassifier()

# 训练模型
clf.fit(X_train, y_train)

# 预测
predictions = clf.predict(X_test)

# 评估模型
accuracy = clf.score(X_test, y_test)
print('Accuracy:', accuracy)