智能审核双11优惠活动

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 假设我们有一个包含历史优惠活动数据的DataFrame
data = pd.read_csv('promotion_data.csv')

# 特征和标签
X = data[['discount_rate', 'gift_quantity', 'sales_volume']]
y = data['is_valid']  # 1表示有效，0表示无效

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估模型
accuracy = accuracy_score(y_test, y_pred)
print(f'模型准确率: {accuracy}')

# 实时审核示例
def audit_promotion(discount_rate, gift_quantity, sales_volume):
    features = [[discount_rate, gift_quantity, sales_volume]]
    prediction = model.predict(features)
    return "有效" if prediction[0] == 1 else "无效"

# 测试实时审核功能
result = audit_promotion(0.2, 5, 1000)
print(f'审核结果: {result}')