信用评分卡建模python代码

信用评分卡模型是一种用于评估个人或企业信贷风险的统计模型，在Python中，我们通常会使用机器学习库如Scikit-Learn来构建这样的模型。以下是一个简单的例子，展示如何使用逻辑回归作为基础模型来创建信用评分卡：

# 导入所需的库
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 accuracy_score, classification_report

# 加载数据集（假设数据集名为credit_data.csv）
data = pd.read_csv('credit_data.csv')

# 数据预处理：选择特征并处理缺失值
X = data[['income', 'debt_to_income_ratio', 'years_of_credit_history', 'credit_utilization']]
y = data['default'] # 标签列
X = X.dropna() # 删除缺失值

# 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)

# 特征缩放
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# 创建逻辑回归模型
model = LogisticRegression()

# 训练模型
model.fit(X_train_scaled, y_train)

# 预测
y_pred = model.predict(X_test_scaled)

# 评估性能
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
print("\nClassification Report:\n", classification_report(y_test, y_pred))

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