地球资源数据云——数据资源详情
银行数据集分类
该数据集《Banking Dataset Classification》主要用于二分类任务，数据形态以表格为主，应用场景偏向金融风控。 题目说明：Predicting if the client will subscribe to a term deposit. 任务类型：表格二分类。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：new_test.csv, new_train.csv。 About Dataset There has been a revenue decline in the Portuguese Bank and they would like to know what actions to take. After investigation, they found that the root cause was that their customers are not investing enough for long term deposits. So the bank would like to identify existing customers that have a higher chance to subscribe for a long term deposit and focus marketing efforts on such customers. Data Set Information The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed. There are two datasets: train.csv with all examples (32950) and 21 inputs including the target feature, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]
摘要概览
该数据集《Banking Dataset Classification》主要用于二分类任务，数据形态以表格为主，应用场景偏向金融风控。 题目说明：Predicting if the client will subscribe to a term deposit.
任务类型：表格二分类。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：new_test.csv, new_train.csv。
About Dataset There has been a revenue decline in the Portuguese Bank and they would like to know what actions to take. After investigation, they found that the root cause was that their customers are not investing enough for long term deposits.
So the bank would like to identify existing customers that have a higher chance to subscribe for a long term deposit and focus marketing efforts on such customers.
Data Set Information
The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed.
There are two datasets: train.csv with all examples (32950) and 21 inputs including the target feature, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]