金融风控实战-Python信用评分卡建模全流程

由于篇幅限制，这里只展示部分代码，具体的信用评分卡建模流程请参考原文链接。

# 导入必要的库
import numpy as np
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 roc_auc_score
 
# 读取数据
data = pd.read_csv('credit_card_data.csv')
 
# 分割特征和目标
X = data.drop(['target'], axis=1)
y = data['target']
 
# 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=0)
 
# 特征工程：标准化
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
 
# 建模：逻辑回归
model = LogisticRegression()
model.fit(X_train_scaled, y_train)
 
# 预测
y_pred = model.predict_proba(X_test_scaled)[:,1]
 
# 评估：AUC
auc_score = roc_auc_score(y_test, y_pred)
print(f'AUC Score: {auc_score}')

这段代码展示了如何进行信用卡欺诈检测，包括数据读取、分割特征和目标、数据划分、特征工程（标准化）、模型训练和预测以及评估模型性能。这是金融场景中常见的一个机器学习项目流程。