Bank Loan Modelling dataset from Kaggle.
Suppose department team wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.
Features:
- Dataset: Sample Data
- Sample Notebook: Banking_Loan.ipynb
| Column | Description |
|---|---|
| Age | Customer age in completed years |
| Experience | Customer professional experience years |
| Income | Customer annual income ($000) |
| ZIP Code | Customer home address ZIP code |
| Family | Customer family size |
| CCAvg | Customer average spending on credit cards per month ($000) |
| Education | Customer Education - Level. 1: Undergraduate; 2: Graduate; 3: Advanced/Professional |
| Mortgage | Customer mortgage value ($000) |
| Personal Loan | Did this customer accept the personal loan offered in the last campaign? |
| Securities Account | Does the customer have a securities account with the bank? |
| CD Account | Does the customer have a certificate of deposit (CD) account with the bank? |
| Online | Does the customer use internet banking service? |
| Credit Card | Does the customer use a credit card issued by Universal Bank? |
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