Explanation of Columns in the Churn Dataset
This dataset contains information about bank customers and whether they have churned (left the bank).
Get dataset here : https://www.kaggle.com/datasets/adammaus/predicting-churn-for-bank-customers
| Column Name | Description |
|---|---|
| RowNumber | The index of the row (not useful for modeling). |
| CustomerId | A unique ID assigned to each customer. |
| Surname | The last name of the customer (not useful for churn prediction). |
| CreditScore | A numerical score representing the customer’s creditworthiness (higher scores indicate better credit history). |
| Geography | The country where the customer resides (e.g., France, Spain, Germany). |
| Gender | The gender of the customer (Male/Female). |
| Age | The age of the customer in years. |
| Tenure | The number of years the customer has been with the bank. |
| Balance | The current balance in the customer’s account. |
| NumOfProducts | The number of bank products the customer is using (e.g., savings accounts, credit cards). |
| HasCrCard | Whether the customer has a credit card (1 = Yes, 0 = No). |
| IsActiveMember | Whether the customer is actively engaging with the bank (1 = Yes, 0 = No). |
| EstimatedSalary | The estimated annual salary of the customer. |
| Exited | The target variable: whether the customer has churned (1 = Yes, 0 = No). |
Insights for Power BI Dashboard:
- Customer Segmentation: Group customers based on credit score, balance, and age.
- Churn Analysis: Compare demographics (e.g., age, gender, country) of customers who churn vs. those who stay.
- Product Usage: Analyze how different numbers of products affect churn rates.
- Customer Engagement: Study the impact of
IsActiveMemberandHasCrCardon churn.