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Customer Credit Risk and Default Data
Finance & Banking Analytics
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About
Credit default behaviour data is presented, which is highly suitable for debugging machine learning models. The data includes well-distributed statistics on income, age, previous loan amounts, and past default behaviours. This information is designed to facilitate faster debugging processes for machine learning techniques such as stacking regressors and stacking classifiers.
Columns
- clientid: Includes the unique client identifier.
- income: Includes the income distribution of clients.
- age: Includes the age of the clients, though some entries are missing or have negative values.
- loan: Includes the existing loan amount for each client.
- default: Includes historical records of clients' defaults, where 1 likely indicates a default and 0 indicates no default.
Distribution
The data is available in a tabular format as a single CSV file named
credit_data.csv, with a size of 86.23 kB. It contains 2000 records across 5 columns. There are 3 missing values in the 'age' column.Usage
This dataset is ideal for computer science applications, particularly for debugging machine learning models. It can be used for tasks involving stacking regressors and stacking classifiers. Its well-distributed statistics make it a useful tool for financial modelling and advanced ensembling techniques.
Coverage
The data provides financial and demographic information without a specified geographical location or time period. It covers individuals with a mean age of approximately 40.8 years, although the age data contains some anomalies like negative values.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: For debugging and testing ML models, especially in the finance sector.
- Financial Analysts: To analyse credit default behaviours and build predictive models.
- Academics and Students: For research and educational purposes in computer science and finance.
Dataset Name Suggestions
- Credit Default Behaviour for ML Debugging
- Customer Credit Risk and Default Data
- Financial Default Analysis Dataset
- ML Model Debugging: Credit Data
- Banking Client Default Behaviour
Attributes
Original Data Source: Customer Credit Risk and Default Data
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