京津冀地区燃气行业用户信用评估数据

信用是衡量公司和个人在经济活动中的信誉和信用状况的指标，在现代社会中扮演着非常重要的角色，其中燃气使用作为日常生活息息相关的一部分，通过其缴费行为分析，可以得出用户的信用评估水平。1、收集每个用户的至少5年历史数据，包括：区域、户号、费用产生日期、开始缴费日期、逾期日期、实际缴费日期、欠费金额、实际缴费金额、实际缴费渠道、账户余额等数据
2、根据数据源计算分析用户行为特征数据（逾期次数、平均逾期天数、最大逾期天数）
缴费及时特征（总缴费次数、按时缴费比例、缴费渠道多样性）、预缴账户余额特征（年平均账户余额、最低账户余额）
3、以用户缴费行为特征数据使用机器学习算法训练模型随机森林，将用户信用等级划分分为几个级别（如优秀、良好、中等、差）。可以根据实际业务需求和模型输出的分布情况来设定这些级别的阈值。

Credit is an indicator measuring the credibility and credit status of companies and individuals in economic activities, and plays a critical role in modern society. As a part closely tied to daily life, gas utility payment behavior can be analyzed to derive users' credit assessment levels.
1. Collect at least 5 years of historical data for each user, including: region, household account number, bill generation date, payment initiation date, overdue date, actual payment date, outstanding amount, actual payment amount, actual payment channel, account balance and other relevant data.
2. Calculate and analyze user behavioral characteristic data based on the collected data sources, including overdue frequency, average overdue days, maximum overdue days; timely payment characteristics (total number of payments, proportion of on-time payments, diversity of payment channels); prepaid account balance characteristics (annual average account balance, minimum account balance).
3. Train a Random Forest machine learning model using the user payment behavioral characteristic data, and divide user credit ratings into multiple levels (e.g., Excellent, Good, Fair, Poor). The thresholds for these levels can be set based on actual business requirements and the distribution of model outputs.