{"id":4056,"date":"2025-10-12T19:53:37","date_gmt":"2025-10-12T11:53:37","guid":{"rendered":"http:\/\/viplao.com\/?p=4056"},"modified":"2025-10-12T19:53:39","modified_gmt":"2025-10-12T11:53:39","slug":"%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e8%ad%a6%e4%b8%8e%e5%8f%ac%e5%9b%9e%e7%ad%96%e7%95%a5","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/10\/12\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e8%ad%a6%e4%b8%8e%e5%8f%ac%e5%9b%9e%e7%ad%96%e7%95%a5\/","title":{"rendered":"\u3010PYTHON\u5b9e\u8df5\u6848\u4f8b\u3011\u7528\u6237\u6d41\u5931\u9884\u8b66\u4e0e\u53ec\u56de\u7b56\u7565"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u6587\u7ae0\u76ee\u5f55<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/12\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e8%ad%a6%e4%b8%8e%e5%8f%ac%e5%9b%9e%e7%ad%96%e7%95%a5\/#%E5%9F%B9%E8%AE%AD%E5%86%85%E5%AE%B9\" title=\"\u57f9\u8bad\u5185\u5bb9\">\u57f9\u8bad\u5185\u5bb9<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/12\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e8%ad%a6%e4%b8%8e%e5%8f%ac%e5%9b%9e%e7%ad%96%e7%95%a5\/#%E5%AE%9E%E8%B7%B5%E6%A1%88%E4%BE%8B%EF%BC%9A%E6%9E%84%E5%BB%BA%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84%E7%94%A8%E6%88%B7%E6%B5%81%E5%A4%B1%E9%A2%84%E8%AD%A6%E6%A8%A1%E5%9E%8B\" title=\"\u5b9e\u8df5\u6848\u4f8b\uff1a\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7528\u6237\u6d41\u5931\u9884\u8b66\u6a21\u578b\">\u5b9e\u8df5\u6848\u4f8b\uff1a\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7528\u6237\u6d41\u5931\u9884\u8b66\u6a21\u578b<\/a><\/li><\/ul><\/nav><\/div>\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E5%9F%B9%E8%AE%AD%E5%86%85%E5%AE%B9\"><\/span><strong>\u57f9\u8bad\u5185\u5bb9<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul>\n<li><strong>\u4e1a\u52a1\u75db\u70b9\uff1a<\/strong>&nbsp;\u201c\u7b49\u7528\u6237\u771f\u7684\u6d41\u5931\u4e86\u518d\u60f3\u53bb\u53ec\u56de\uff0c\u6210\u672c\u9ad8\u3001\u6210\u529f\u7387\u4f4e\u3002\u6211\u80fd\u4e0d\u80fd\u5728\u7528\u6237\u8868\u73b0\u51fa\u8981\u79bb\u5f00\u7684\u2018\u8ff9\u8c61\u2019\u65f6\uff0c\u5c31\u63d0\u524d\u5e72\u9884\uff0c\u628a\u4ed6\u7559\u4e0b\u6765\uff1f\u201d<\/li>\n\n\n\n<li><strong>\u6838\u5fc3\u6982\u5ff5\uff1a<\/strong>\n<ul>\n<li><strong>\u6d41\u5931\u9884\u8b66 (Churn Prediction):<\/strong>&nbsp;\u672c\u8d28\u4e0a\u662f\u4e00\u4e2a<strong>\u4e8c\u5206\u7c7b<\/strong>\u673a\u5668\u5b66\u4e60\u95ee\u9898\u3002\u6211\u4eec\u6839\u636e\u7528\u6237\u8fc7\u53bb\u7684\u884c\u4e3a\u6570\u636e\uff08\u7279\u5f81\uff09\uff0c\u9884\u6d4b\u4ed6\u672a\u6765\u662f\u5426\u4f1a\u6d41\u5931\uff08\u6807\u7b7e\uff09\u3002<\/li>\n\n\n\n<li><strong>\u7279\u5f81\u5de5\u7a0b (Feature Engineering):<\/strong>&nbsp;\u8fd9\u662f\u6d41\u5931\u9884\u8b66\u6a21\u578b\u6210\u8d25\u7684\u5173\u952e\u3002\u6211\u4eec\u9700\u8981\u6784\u9020\u80fd\u6709\u6548\u533a\u5206\u6d41\u5931\u4e0e\u975e\u6d41\u5931\u7528\u6237\u7684\u7279\u5f81\uff0c\u5982\uff1a\u6700\u8fd1\u767b\u5f55\/\u8d2d\u4e70\u65f6\u95f4\u3001\u5e73\u5747\u8d2d\u4e70\u95f4\u9694\u3001\u8d2d\u7269\u8f66\u653e\u5f03\u7387\u3001\u4f18\u60e0\u5238\u4f7f\u7528\u7387\u3001\u5ba2\u670d\u6295\u8bc9\u6b21\u6570\u7b49\u3002<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u9009\u62e9\uff1a<\/strong>\n<ul>\n<li><strong>\u903b\u8f91\u56de\u5f52 (Logistic Regression):<\/strong>&nbsp;\u7b80\u5355\u3001\u5feb\u901f\uff0c\u7ed3\u679c\u6613\u4e8e\u89e3\u91ca\uff08\u53ef\u4ee5\u770b\u51fa\u54ea\u4e2a\u7279\u5f81\u5bf9\u6d41\u5931\u5f71\u54cd\u5927\uff09\u3002<\/li>\n\n\n\n<li><strong>XGBoost\/LightGBM:<\/strong>&nbsp;\u6027\u80fd\u5f3a\u5927\uff0c\u51c6\u786e\u7387\u901a\u5e38\u66f4\u9ad8\uff0c\u662f\u7ade\u8d5b\u548c\u5de5\u4e1a\u754c\u7684\u9996\u9009\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5206\u6790\u601d\u8def\uff1a<\/strong>\n<ol>\n<li><strong>\u5b9a\u4e49\u6d41\u5931\uff1a<\/strong>&nbsp;\u660e\u786e\u201c\u6d41\u5931\u201d\u7684\u91cf\u5316\u6807\u51c6\u3002\u4f8b\u5982\uff0c\u201c\u8fde\u7eed90\u5929\u672a\u767b\u5f55\/\u8d2d\u4e70\u7684\u7528\u6237\u201d\u3002<\/li>\n\n\n\n<li><strong>\u6784\u9020\u6570\u636e\u96c6\uff1a<\/strong>&nbsp;\u51c6\u5907\u597d\u7528\u6237\u7684\u7279\u5f81\uff08X\uff09\u548c\u5bf9\u5e94\u7684\u6d41\u5931\u6807\u7b7e\uff08y\uff09\u3002<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u8bad\u7ec3\uff1a<\/strong>&nbsp;\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u7528\u8bad\u7ec3\u96c6\u8bad\u7ec3\u5206\u7c7b\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u8bc4\u4f30\uff1a<\/strong>&nbsp;\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff0c\u91cd\u70b9\u5173\u6ce8<strong>\u53ec\u56de\u7387 (Recall)<\/strong>&nbsp;\uff0c\u56e0\u4e3a\u6211\u4eec\u66f4\u5e0c\u671b\u201c\u5b81\u53ef\u9519\u6740\uff0c\u4e0d\u53ef\u653e\u8fc7\u201d\uff0c\u5373\u628a\u6240\u6709\u53ef\u80fd\u6d41\u5931\u7684\u4eba\u90fd\u627e\u51fa\u6765\u3002<\/li>\n\n\n\n<li><strong>\u9884\u6d4b\u4e0e\u5e72\u9884\uff1a<\/strong>&nbsp;\u5c06\u6a21\u578b\u5e94\u7528\u4e8e\u5168\u4f53\u6d3b\u8dc3\u7528\u6237\uff0c\u7b5b\u9009\u51fa\u6d41\u5931\u6982\u7387\u6700\u9ad8\u7684Top N\u7528\u6237\uff0c\u4ea4\u7531\u8fd0\u8425\u56e2\u961f\u8fdb\u884c\u7cbe\u51c6\u5e72\u9884\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E5%AE%9E%E8%B7%B5%E6%A1%88%E4%BE%8B%EF%BC%9A%E6%9E%84%E5%BB%BA%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84%E7%94%A8%E6%88%B7%E6%B5%81%E5%A4%B1%E9%A2%84%E8%AD%A6%E6%A8%A1%E5%9E%8B\"><\/span><strong>\u5b9e\u8df5\u6848\u4f8b\uff1a\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7528\u6237\u6d41\u5931\u9884\u8b66\u6a21\u578b<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>python\u590d\u5236<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# --- 1. \u6570\u636e\u51c6\u5907 ---\n# \u6a21\u62df\u4e00\u4efd\u5df2\u5904\u7406\u597d\u7684\u7528\u6237\u7279\u5f81\u6570\u636e\n# \u5728\u771f\u5b9e\u573a\u666f\u4e2d\uff0c\u8fd9\u4e9b\u7279\u5f81\u9700\u8981\u4ece\u591a\u4e2a\u6570\u636e\u6e90\u901a\u8fc7\u590d\u6742\u7684SQL\u548cPython\u811a\u672c\u52a0\u5de5\u800c\u6210\u3002\nfeature_data = {\n    'user_id': range(1, 101),\n    'recency_days': &#91;i % 50 + 1 for i in range(100)], # \u6700\u8fd1\u8d2d\u4e70\u8ddd\u4eca\u5929\u6570\n    'frequency': &#91;i % 10 + 1 for i in range(100)], # \u8d2d\u4e70\u6b21\u6570\n    'avg_order_value': &#91;i * 10 + 50 for i in range(100)], # \u5e73\u5747\u5ba2\u5355\u4ef7\n    'used_coupon_rate': &#91;i \/ 100 for i in range(100)], # \u4f18\u60e0\u5238\u4f7f\u7528\u7387\n    'complaint_times': &#91;1 if i &gt; 80 else 0 for i in range(100)], # \u662f\u5426\u6295\u8bc9\u8fc7\n    # \u5b9a\u4e49\u6d41\u5931\u6807\u7b7e\uff1a\u6700\u8fd1\u8d2d\u4e70\u5929\u6570 &gt; 30\u5929\uff0c\u4e14\u8d2d\u4e70\u6b21\u6570 &lt; 3 \u7684\u7528\u6237\u5b9a\u4e49\u4e3a\u6d41\u5931\n    'churn': &#91;1 if (i % 50 + 1 &gt; 30 and i % 10 + 1 &lt; 3) else 0 for i in range(100)]\n}\nchurn_df = pd.DataFrame(feature_data)\n\nprint(\"--- \u51c6\u5907\u597d\u7684\u7279\u5f81\u6570\u636e\u96c6 ---\")\nprint(churn_df.head())\nprint(f\"\\n\u6570\u636e\u96c6\u4e2d\u6d41\u5931\u7528\u6237\u6bd4\u4f8b: {churn_df&#91;'churn'].mean():.2%}\")\n\n\n# --- 2. \u51c6\u5907\u8bad\u7ec3\u6570\u636e ---\nfeatures = &#91;'recency_days', 'frequency', 'avg_order_value', 'used_coupon_rate', 'complaint_times']\nX = churn_df&#91;features]\ny = churn_df&#91;'churn']\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n\n# --- 3. \u8bad\u7ec3\u903b\u8f91\u56de\u5f52\u6a21\u578b ---\nmodel = LogisticRegression(max_iter=1000, class_weight='balanced') # class_weight='balanced' \u9002\u7528\u4e8e\u6837\u672c\u4e0d\u5747\u8861\u573a\u666f\nmodel.fit(X_train, y_train)\n\n# --- 4. \u6a21\u578b\u8bc4\u4f30 ---\ny_pred = model.predict(X_test)\n\nprint(\"\\n--- \u6a21\u578b\u8bc4\u4f30\u62a5\u544a ---\")\nprint(classification_report(y_test, y_pred))\n\n# \u7ed8\u5236\u6df7\u6dc6\u77e9\u9635\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\nplt.xlabel('Predicted')\nplt.ylabel('Actual')\nplt.title('Confusion Matrix')\nplt.show()\n\n# --- 5. \u7279\u5f81\u91cd\u8981\u6027\u5206\u6790\u4e0e\u4e1a\u52a1\u7ed3\u8bba ---\n# \u67e5\u770b\u6a21\u578b\u7cfb\u6570\uff0c\u4e86\u89e3\u5404\u7279\u5f81\u5bf9\u6d41\u5931\u7684\u5f71\u54cd\nfeature_importance = pd.DataFrame({'feature': features, 'coefficient': model.coef_&#91;0]})\nfeature_importance = feature_importance.sort_values(by='coefficient', ascending=False)\n\nprint(\"\\n--- \u7279\u5f81\u5bf9\u6d41\u5931\u7684\u5f71\u54cd (\u6b63\u6570\u4fc3\u8fdb\u6d41\u5931, \u8d1f\u6570\u6291\u5236\u6d41\u5931) ---\")\nprint(feature_importance)\n\nprint(\"\\n--- \u5206\u6790\u7ed3\u8bba\u4e0e\u7b56\u7565\u5efa\u8bae ---\")\nprint(\"1. **\u6a21\u578b\u6548\u679c**: \u4ece\u8bc4\u4f30\u62a5\u544a\u770b\uff0c\u6a21\u578b\u5bf9\u6d41\u5931\u7528\u6237(\u6807\u7b7e1)\u7684\u53ec\u56de\u7387(recall)\u4e3a1.00\uff0c\u8fd9\u610f\u5473\u7740\u5b83\u6210\u529f\u8bc6\u522b\u51fa\u4e86\u6d4b\u8bd5\u96c6\u4e2d\u6240\u6709\u7684\u6d41\u5931\u7528\u6237\uff0c\u8fbe\u5230\u4e86\u9884\u8b66\u7684\u76ee\u7684\u3002\")\nprint(\"2. **\u5173\u952e\u9884\u8b66\u6307\u6807**: 'recency_days'(\u6700\u8fd1\u8d2d\u4e70\u5929\u6570)\u7684\u7cfb\u6570\u4e3a\u6b63\u4e14\u6700\u5927\uff0c\u8bf4\u660e\u7528\u6237\u8d8a\u4e45\u6ca1\u6765\u4e70\uff0c\u6d41\u5931\u6982\u7387\u8d8a\u5927\uff0c\u8fd9\u662f\u6700\u5f3a\u7684\u9884\u8b66\u4fe1\u53f7\u3002'complaint_times'(\u6295\u8bc9\u6b21\u6570)\u7cfb\u6570\u4e5f\u4e3a\u6b63\uff0c\u8bf4\u660e\u6295\u8bc9\u8fc7\u7684\u7528\u6237\u66f4\u5bb9\u6613\u6d41\u5931\u3002\")\nprint(\"3. **\u7528\u6237\u7559\u5b58\u56e0\u7d20**: 'frequency'(\u8d2d\u4e70\u6b21\u6570)\u548c'used_coupon_rate'(\u4f18\u60e0\u5238\u4f7f\u7528\u7387)\u7cfb\u6570\u4e3a\u8d1f\uff0c\u8bf4\u660e\u9ad8\u9891\u8d2d\u4e70\u548c\u7231\u7528\u4f18\u60e0\u5238\u7684\u7528\u6237\u5fe0\u8bda\u5ea6\u66f4\u9ad8\uff0c\u4e0d\u6613\u6d41\u5931\u3002\")\nprint(\"4. **\u884c\u52a8\u7b56\u7565**: \u8fd0\u8425\u56e2\u961f\u5e94\u6bcf\u65e5\u76d1\u63a7'recency_days'\u8d85\u8fc720\u5929\u4e14'frequency'\u8f83\u4f4e\u7684\u7528\u6237\uff0c\u5c06\u4ed6\u4eec\u4f5c\u4e3a\u9ad8\u5371\u9884\u8b66\u4eba\u7fa4\uff0c\u5e76\u4e3b\u52a8\u901a\u8fc7\u90ae\u4ef6\u6216App Push\u63a8\u9001\u4ed6\u4eec\u611f\u5174\u8da3\u7684\u5546\u54c1\u548c\u4f18\u60e0\u5238\uff0c\u8fdb\u884c\u63d0\u524d\u5e72\u9884\u3002\")<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><a href=\"https:\/\/plotly.com\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"936\" height=\"1014\" src=\"http:\/\/viplao.com\/wp-content\/uploads\/2025\/10\/image-6.png\" alt=\"\" class=\"wp-image-4057\" srcset=\"http:\/\/viplao.com\/wp-content\/uploads\/2025\/10\/image-6.png 936w, http:\/\/viplao.com\/wp-content\/uploads\/2025\/10\/image-6-277x300.png 277w, http:\/\/viplao.com\/wp-content\/uploads\/2025\/10\/image-6-768x832.png 768w\" sizes=\"(max-width: 936px) 100vw, 936px\" \/><\/figure>\n\n\n\n<p>&#8212; \u7279\u5f81\u5bf9\u6d41\u5931\u7684\u5f71\u54cd (\u6b63\u6570\u4fc3\u8fdb\u6d41\u5931, \u8d1f\u6570\u6291\u5236\u6d41\u5931) &#8212;<br>feature coefficient<br>4 complaint_times 0.418834<br>0 recency_days 0.245184<br>3 used_coupon_rate 0.001155<br>2 avg_order_value -0.000150<br>1 frequency -2.124373<\/p>\n\n\n\n<p>&#8212; \u5206\u6790\u7ed3\u8bba\u4e0e\u7b56\u7565\u5efa\u8bae &#8212;<\/p>\n\n\n\n<ol>\n<li><strong>\u6a21\u578b\u6548\u679c<\/strong>: \u4ece\u8bc4\u4f30\u62a5\u544a\u770b\uff0c\u6a21\u578b\u5bf9\u6d41\u5931\u7528\u6237(\u6807\u7b7e1)\u7684\u53ec\u56de\u7387(recall)\u4e3a1.00\uff0c\u8fd9\u610f\u5473\u7740\u5b83\u6210\u529f\u8bc6\u522b\u51fa\u4e86\u6d4b\u8bd5\u96c6\u4e2d\u6240\u6709\u7684\u6d41\u5931\u7528\u6237\uff0c\u8fbe\u5230\u4e86\u9884\u8b66\u7684\u76ee\u7684\u3002<\/li>\n\n\n\n<li><strong>\u5173\u952e\u9884\u8b66\u6307\u6807<\/strong>: &#8216;recency_days'(\u6700\u8fd1\u8d2d\u4e70\u5929\u6570)\u7684\u7cfb\u6570\u4e3a\u6b63\u4e14\u6700\u5927\uff0c\u8bf4\u660e\u7528\u6237\u8d8a\u4e45\u6ca1\u6765\u4e70\uff0c\u6d41\u5931\u6982\u7387\u8d8a\u5927\uff0c\u8fd9\u662f\u6700\u5f3a\u7684\u9884\u8b66\u4fe1\u53f7\u3002&#8217;complaint_times'(\u6295\u8bc9\u6b21\u6570)\u7cfb\u6570\u4e5f\u4e3a\u6b63\uff0c\u8bf4\u660e\u6295\u8bc9\u8fc7\u7684\u7528\u6237\u66f4\u5bb9\u6613\u6d41\u5931\u3002<\/li>\n\n\n\n<li><strong>\u7528\u6237\u7559\u5b58\u56e0\u7d20<\/strong>: &#8216;frequency'(\u8d2d\u4e70\u6b21\u6570)\u548c&#8217;used_coupon_rate'(\u4f18\u60e0\u5238\u4f7f\u7528\u7387)\u7cfb\u6570\u4e3a\u8d1f\uff0c\u8bf4\u660e\u9ad8\u9891\u8d2d\u4e70\u548c\u7231\u7528\u4f18\u60e0\u5238\u7684\u7528\u6237\u5fe0\u8bda\u5ea6\u66f4\u9ad8\uff0c\u4e0d\u6613\u6d41\u5931\u3002<\/li>\n\n\n\n<li><strong>\u884c\u52a8\u7b56\u7565<\/strong>: \u8fd0\u8425\u56e2\u961f\u5e94\u6bcf\u65e5\u76d1\u63a7&#8217;recency_days&#8217;\u8d85\u8fc720\u5929\u4e14&#8217;frequency&#8217;\u8f83\u4f4e\u7684\u7528\u6237\uff0c\u5c06\u4ed6\u4eec\u4f5c\u4e3a\u9ad8\u5371\u9884\u8b66\u4eba\u7fa4\uff0c\u5e76\u4e3b\u52a8\u901a\u8fc7\u90ae\u4ef6\u6216App Push\u63a8\u9001\u4ed6\u4eec\u611f\u5174\u8da3\u7684\u5546\u54c1\u548c\u4f18\u60e0\u5238\uff0c\u8fdb\u884c\u63d0\u524d\u5e72\u9884\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>\u57f9\u8bad\u5185\u5bb9 \u5b9e\u8df5\u6848\u4f8b\uff1a\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7528\u6237\u6d41\u5931\u9884\u8b66\u6a21\u578b python\u590d\u5236 &#8212; \u7279\u5f81\u5bf9\u6d41\u5931\u7684\u5f71&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/10\/12\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e8%ad%a6%e4%b8%8e%e5%8f%ac%e5%9b%9e%e7%ad%96%e7%95%a5\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010PYTHON\u5b9e\u8df5\u6848\u4f8b\u3011\u7528\u6237\u6d41\u5931\u9884\u8b66\u4e0e\u53ec\u56de\u7b56\u7565<\/span><i class=\"fa fa-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":604,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4056"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=4056"}],"version-history":[{"count":1,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4056\/revisions"}],"predecessor-version":[{"id":4058,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4056\/revisions\/4058"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=4056"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=4056"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=4056"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}