generated from Python-2026Spring/assignment-05-final-project-template
68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
# predict.py - 客户流失预测接口
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import joblib
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import pandas as pd
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# 加载保存好的模型（关键：路径要和save_model.py生成的pkl文件一致）
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try:
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model = joblib.load('telco_churn_model.pkl')
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print("✅ 模型加载成功！")
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except FileNotFoundError:
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print("❌ 未找到模型文件，请先运行save_model.py生成telco_churn_model.pkl")
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exit()
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def predict_churn(customer_data):
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"""
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预测客户流失概率
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参数：customer_data - DataFrame，包含客户的所有特征
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返回：预测结果（是否流失+流失概率）
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"""
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# 新增特征组合（必须和训练时一致，否则预测不准）
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customer_data['tenure_monthly'] = customer_data['tenure'] * customer_data['MonthlyCharges']
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customer_data['tenure_ratio'] = customer_data['tenure'] / (customer_data['TotalCharges'] + 1)
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customer_data['monthly_total_ratio'] = customer_data['MonthlyCharges'] / (customer_data['TotalCharges'] + 1)
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# 执行预测
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churn_pred = model.predict(customer_data) # 0=未流失，1=流失
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churn_prob = model.predict_proba(customer_data)[:, 1] # 流失概率
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# 整理结果
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result = pd.DataFrame({
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'客户ID': customer_data['customerID'],
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'是否流失（0=否，1=是）': churn_pred,
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'流失概率': churn_prob.round(3) # 保留3位小数
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})
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return result
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# 示例：预测1个测试客户的流失概率
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if __name__ == "__main__":
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# 构造测试客户数据（特征和原数据集一致）
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test_customer = pd.DataFrame({
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'customerID': ['TEST001'], # 客户ID
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'tenure': [12], # 在网时长（月）
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'MonthlyCharges': [69.99], # 月消费
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'TotalCharges': [839.88], # 总消费
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'Contract': ['Month-to-month'], # 合同类型（月付）
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'InternetService': ['Fiber optic'], # 光纤上网
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'PaymentMethod': ['Electronic check'], # 电子支票支付
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'OnlineSecurity': ['No'], # 无网络安全服务
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'TechSupport': ['No'], # 无技术支持
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'PaperlessBilling': ['Yes'], # 无纸化账单
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# 以下是其他必填特征（按原数据集补充）
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'gender': ['Female'],
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'SeniorCitizen': [0],
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'Partner': ['Yes'],
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'Dependents': ['No'],
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'PhoneService': ['Yes'],
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'MultipleLines': ['No'],
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'OnlineBackup': ['Yes'],
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'DeviceProtection': ['No'],
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'StreamingTV': ['Yes'],
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'StreamingMovies': ['No']
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})
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# 调用预测函数
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result = predict_churn(test_customer)
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# 打印结果
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print("\n📊 客户流失预测结果：")
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print(result) |