金融实验

本次教学赛是陈博士发起的数据分析系列赛事第1场 —— 银行客户认购产品预测

赛题以银行产品认购预测为背景，想让你来预测下客户是否会购买银行的产品。在和客户沟通的过程中，我们记录了和客户联系的次数，上一次联系的时长，上一次联系的时间间隔，同时在银行系统中我们保存了客户的基本信息，包括：年龄、职业、婚姻、之前是否有违约、是否有房贷等信息，此外我们还统计了当前市场的情况：就业、消费信息、银行同业拆解率等。

用户购买预测是数字化营销领域中的重要应用场景，通过这道赛题，鼓励学习者利用营销活动信息，为企业提供销售策略，也为消费者提供更适合的商品推荐。

金融数据分析比赛的目的是为了更好地带动数据科学初学者一起玩起来，因此我们鼓励所有选手，基于赛题发表notebook分享，内容包含但不限于对赛题的理解、数据分析及可视化、算法模型的分析以及数据分析思路等内容。

This educational competition is the first installment of the data analysis series initiated by Dr. Chen, themed on bank customer product subscription prediction.

The competition task is set against the background of bank product subscription prediction, requiring participants to forecast whether a customer will purchase a bank product. During customer outreach, we have recorded the total number of contacts with the customer, the duration of the last contact, and the time elapsed since the last contact. In addition, the bank’s system stores the customer’s basic profile information, including age, occupation, marital status, prior default history, whether the customer holds a mortgage, and other relevant details. Furthermore, current market statistics such as employment conditions, consumer data, and interbank offered rates are also collected for this task.

Customer purchase prediction is a critical application scenario in the field of digital marketing. Through this competition, participants are encouraged to leverage marketing campaign data to develop sales strategies for enterprises and provide more tailored product recommendations for consumers.

The goal of this financial data analysis competition is to better engage data science beginners in learning and practice. Therefore, all participants are encouraged to publish notebook-based sharing content, which may include but is not limited to their understanding of the competition task, data analysis and visualization work, algorithm model analysis, and data analysis methodologies, among other relevant aspects.

搜集汇总

背景与挑战

背景概述

该数据集为银行客户认购产品预测赛题提供，包含客户联系记录、基本信息及市场情况等数据，适用于数字化营销和产品推荐场景的分析与建模。