Churn for Bank Customers

### Content

- RowNumber—corresponds to the record (row) number and has no effect on the output.
- CustomerId—contains random values and has no effect on customer leaving the bank.
- Surname—the surname of a customer has no impact on their decision to leave the bank.
- CreditScore—can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.
- Geography—a customer’s location can affect their decision to leave the bank.
- Gender—it’s interesting to explore whether gender plays a role in a customer leaving the bank.
- Age—this is certainly relevant, since older customers are less likely to leave their bank than younger ones.
- Tenure—refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.
- Balance—also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.
- NumOfProducts—refers to the number of products that a customer has purchased through the bank.
- HasCrCard—denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.
- IsActiveMember—active customers are less likely to leave the bank.
- EstimatedSalary—as with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
- Exited—whether or not the customer left the bank.

### Acknowledgements

As we know, it is much more expensive to sign in a new client than keeping an existing one.

It is advantageous for banks to know what leads a client towards the decision to leave the company.

Churn prevention allows companies to develop loyalty programs and retention campaigns to keep as many customers as possible.

### 内容

- RowNumber—对应记录（行）编号，对输出无影响。
- CustomerId—包含随机值，对客户离开银行无影响。
- Surname—客户的姓氏对其离开银行的决定无影响。
- CreditScore—可能对客户流失有影响，因为信用评分较高的客户离开银行的可能性较低。
- Geography—客户的位置可能影响其离开银行的决定。
- Gender—探讨性别在客户离开银行中是否发挥作用，颇为有趣。
- Age—年龄无疑是相关的，因为与年轻客户相比，年龄较大的客户不太可能离开他们的银行。
- Tenure—指客户作为银行客户的年数。通常，老客户更加忠诚，离开银行的可能性较低。
- Balance—也是一个很好的客户流失指标，因为在账户中有更高余额的人比余额较低的人不太可能离开银行。
- NumOfProducts—指客户通过银行购买的产品数量。
- HasCrCard—表示客户是否拥有信用卡。这一列也很相关，因为拥有信用卡的人离开银行的可能性较低。
- IsActiveMember—活跃客户离开银行的可能性较低。
- EstimatedSalary—与余额类似，与高薪相比，薪水较低的人更有可能离开银行。
- Exited—表示客户是否离开了银行。

### 致谢

众所周知，吸引新客户比留住现有客户成本高昂得多。
银行了解导致客户离开公司的因素是有益的。
防止客户流失使公司能够开发忠诚度计划和保留活动，以尽可能留住更多客户。

搜集汇总

背景与挑战

背景概述

该数据集包含银行客户流失预测相关的14个特征，如信用评分、地理位置、年龄等，目标变量为'Exited'表示客户是否流失，适用于客户流失分析和预测建模。