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