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