双11企业信用画像推荐

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# 假设df包含以下列：'transaction_amount', 'return_rate', 'complaint_count', 'social_media_score'
features = ['transaction_amount', 'return_rate', 'complaint_count', 'social_media_score']
X = df[features]
y = df['credit_rating']  # 假设已有信用评级标签

# 数据标准化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 训练逻辑回归模型
model = LogisticRegression()
model.fit(X_scaled, y)

# 预测新商家的信用评分
new_business_data = [[1000, 0.05, 3, 4.5]]  # 示例新商家数据
predicted_credit_score = model.predict(scaler.transform(new_business_data))
print(f"Predicted Credit Score: {predicted_credit_score}")