f47c7d7196
添加完整的客户流失预测系统，包括数据处理、模型训练、预测和行动建议功能。主要包含以下模块： 1. 数据预处理流水线（Polars + Pandera） 2. 机器学习模型训练（LightGBM + Logistic Regression） 3. AI Agent预测和建议工具 4. Streamlit交互式Web界面 5. 完整的课程设计报告文档
200 lines
8.3 KiB
Python
200 lines
8.3 KiB
Python
from pydantic import BaseModel, Field
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from typing import Dict, Any, List
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import polars as pl
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import joblib
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from data_processing import preprocess_data
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# 定义输入输出模型
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class CustomerData(BaseModel):
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"""客户数据模型"""
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gender: str = Field(..., description="性别: Male 或 Female")
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SeniorCitizen: int = Field(..., description="是否为老年人: 0 或 1")
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Partner: str = Field(..., description="是否有伴侣: Yes 或 No")
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Dependents: str = Field(..., description="是否有家属: Yes 或 No")
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tenure: int = Field(..., description="在网时长，单位为月")
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PhoneService: str = Field(..., description="是否开通电话服务: Yes 或 No")
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MultipleLines: str = Field(..., description="是否开通多条线路: Yes、No 或 No phone service")
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InternetService: str = Field(..., description="网络服务类型: DSL、Fiber optic 或 No")
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OnlineSecurity: str = Field(..., description="是否开通在线安全服务: Yes、No 或 No internet service")
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OnlineBackup: str = Field(..., description="是否开通在线备份服务: Yes、No 或 No internet service")
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DeviceProtection: str = Field(..., description="是否开通设备保护服务: Yes、No 或 No internet service")
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TechSupport: str = Field(..., description="是否开通技术支持服务: Yes、No 或 No internet service")
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StreamingTV: str = Field(..., description="是否开通流媒体电视服务: Yes、No 或 No internet service")
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StreamingMovies: str = Field(..., description="是否开通流媒体电影服务: Yes、No 或 No internet service")
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Contract: str = Field(..., description="合同类型: Month-to-month、One year 或 Two year")
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PaperlessBilling: str = Field(..., description="是否使用无纸化账单: Yes 或 No")
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PaymentMethod: str = Field(..., description="支付方式")
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MonthlyCharges: float = Field(..., description="月费用")
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TotalCharges: float = Field(..., description="总费用")
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class ChurnPrediction(BaseModel):
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"""客户流失预测结果"""
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prediction: int = Field(..., description="预测结果: 0 表示不流失，1 表示流失")
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probability: float = Field(..., description="流失概率")
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model_used: str = Field(..., description="使用的模型")
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class ActionSuggestion(BaseModel):
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"""基于预测结果的行动建议"""
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customer_id: str = Field(..., description="客户ID")
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prediction: int = Field(..., description="预测结果: 0 表示不流失，1 表示流失")
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probability: float = Field(..., description="流失概率")
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suggestions: List[str] = Field(..., description="可执行的行动建议")
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# Agent工具类
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class ChurnPredictionAgent:
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def __init__(self):
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# 加载最佳模型（使用LightGBM，因为它通常表现更好）
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self.model = joblib.load("models/lightgbm_model.pkl")
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self.model_name = "lightgbm"
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# 工具1：ML预测工具
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def predict_churn(self, customer_data: CustomerData) -> ChurnPrediction:
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"""
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预测客户是否会流失
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Args:
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customer_data: 客户数据
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Returns:
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ChurnPrediction: 预测结果
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"""
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# 将客户数据转换为Polars DataFrame
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customer_dict = customer_data.model_dump()
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df = pl.DataFrame([customer_dict])
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# 数据预处理（使用专门的单个客户预处理函数）
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from data_processing import preprocess_single_customer
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X_np = preprocess_single_customer(df)
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# 预测
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probability = self.model.predict_proba(X_np)[0, 1]
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prediction = 1 if probability >= 0.5 else 0
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return ChurnPrediction(
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prediction=prediction,
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probability=probability,
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model_used=self.model_name
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)
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# 工具2：行动建议工具
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def get_action_suggestions(self, customer_id: str, prediction: int,
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probability: float, customer_data: CustomerData) -> ActionSuggestion:
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"""
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基于预测结果给出可执行的行动建议
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Args:
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customer_id: 客户ID
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prediction: 预测结果
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probability: 流失概率
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customer_data: 客户数据
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Returns:
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ActionSuggestion: 行动建议
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"""
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suggestions = []
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if prediction == 1:
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# 高流失风险客户
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suggestions.append(f"客户 {customer_id} 有 {probability:.2%} 的概率会流失，需要重点关注")
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# 基于客户特征给出具体建议
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if customer_data.Contract == "Month-to-month":
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suggestions.append("建议提供长期合同折扣，鼓励客户转为一年或两年合同")
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if customer_data.TechSupport == "No":
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suggestions.append("建议提供免费的技术支持服务，提高客户满意度")
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if customer_data.OnlineSecurity == "No":
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suggestions.append("建议提供免费的在线安全服务，增加客户粘性")
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if customer_data.tenure < 12:
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suggestions.append("建议提供新客户忠诚度奖励计划，鼓励客户继续使用服务")
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if customer_data.MonthlyCharges > 70:
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suggestions.append(f"客户月费用较高 ({customer_data.MonthlyCharges} 元)，建议提供费用优化方案")
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else:
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# 低流失风险客户
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suggestions.append(f"客户 {customer_id} 流失风险较低 ({probability:.2%})，可维持现有服务")
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# 基于客户特征给出具体建议
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if customer_data.Contract == "Month-to-month":
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suggestions.append("建议定期发送满意度调查，了解客户需求")
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if customer_data.tenure >= 24:
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suggestions.append("建议提供忠诚客户专属优惠，巩固客户关系")
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return ActionSuggestion(
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customer_id=customer_id,
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prediction=prediction,
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probability=probability,
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suggestions=suggestions
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)
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# 工具3：批量预测工具（额外工具）
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def batch_predict(self, customer_data_list: List[CustomerData]) -> List[ChurnPrediction]:
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"""
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批量预测客户是否会流失
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Args:
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customer_data_list: 客户数据列表
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Returns:
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List[ChurnPrediction]: 预测结果列表
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"""
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results = []
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for customer_data in customer_data_list:
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result = self.predict_churn(customer_data)
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results.append(result)
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return results
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# 测试Agent
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if __name__ == "__main__":
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# 创建Agent实例
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agent = ChurnPredictionAgent()
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# 测试数据
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test_customer = CustomerData(
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gender="Male",
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SeniorCitizen=0,
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Partner="Yes",
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Dependents="No",
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tenure=12,
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PhoneService="Yes",
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MultipleLines="No",
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InternetService="Fiber optic",
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OnlineSecurity="No",
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OnlineBackup="Yes",
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DeviceProtection="No",
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TechSupport="No",
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StreamingTV="Yes",
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StreamingMovies="Yes",
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Contract="Month-to-month",
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PaperlessBilling="Yes",
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PaymentMethod="Electronic check",
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MonthlyCharges=79.85,
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TotalCharges=977.6
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)
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# 1. 使用ML预测工具
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print("=== 使用ML预测工具 ===")
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prediction_result = agent.predict_churn(test_customer)
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print(f"预测结果: {'会流失' if prediction_result.prediction == 1 else '不会流失'}")
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print(f"流失概率: {prediction_result.probability:.2%}")
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print(f"使用模型: {prediction_result.model_used}")
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# 2. 使用行动建议工具
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print("\n=== 使用行动建议工具 ===")
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suggestions = agent.get_action_suggestions(
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customer_id="TEST-123",
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prediction=prediction_result.prediction,
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probability=prediction_result.probability,
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customer_data=test_customer
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)
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print(f"客户ID: {suggestions.customer_id}")
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print(f"预测结果: {'会流失' if suggestions.prediction == 1 else '不会流失'}")
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print(f"流失概率: {suggestions.probability:.2%}")
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print("行动建议:")
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for i, suggestion in enumerate(suggestions.suggestions, 1):
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print(f" {i}. {suggestion}")
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