import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pickle
from sklearn.metrics import classification_report
from imblearn.over_sampling import SMOTE
# 加载数据
data =
# 显示前五行的数据
print()
# 选择自变量和因变量
X = data.drop(['SeriousDlqin2yrs', 'Unnamed: 0'], axis=1)
y = data['SeriousDlqin2yrs']
# 分割训练集和测试集（测试集20%）
X_train, X_test, y_train, y_test = (, random_state=42)
# 训练Logistic回归模型（最大迭代次数为1000次）
model =
#训练 Logistic 回归模型
# 保存模型
with open('2.2.1_model.pkl', 'wb') as file:
pickle.
# 预测并保存结果
y_pred =
pd.DataFrame(y_pred, columns=['预测结果']).to_csv('2.2.1_results.txt', index=False)
# 生成测试报告
report = classification_report(y_test, y_pred, zero_division=1)
with open('2.2.1_report.txt', 'w') as file:
file.write(report)
# 分析测试结果
accuracy =
print(f"模型准确率: {accuracy:.2f}")
# 处理数据不平衡
smote = SMOTE(random_state=42)
X_resampled, y_resampled =
# 重新训练模型
# 重新预测
y_pred_resampled =
# 保存新结果
pd.DataFrame(y_pred_resampled, columns=['预测结果']).to_csv('2.2.1_results_xg.txt', index=False)
# 生成新的测试报告
report_resampled = classification_report(y_test, y_pred_resampled, zero_division=1)
with open('2.2.1_report_xg.txt', 'w') as file:
file.write(report_resampled)
# 分析新的测试结果
accuracy_resampled =
print(f"重新采样后的模型准确率: {accuracy_resampled:.2f}")