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Credit Card Approval Prediction Dataset
Finance & Banking Analytics
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About
This dataset is a cleaned version of a UCI machine learning repository dataset concerning credit card approvals. Its primary purpose is to enable the prediction of individuals who are successful in applying for a credit card. Missing values have been populated, and feature names and categorical descriptions have been inferred to provide more context and enhance ease of use. This makes it a valuable resource for developing predictive models for credit risk assessment.
Columns
- Gender: Indicates the gender of the applicant, where 0 represents Female and 1 represents Male.
- Age: The age of the applicant, expressed in years.
- Debt: Represents the outstanding debt of the applicant, with this feature having been scaled.
- Married: Denotes the marital status of the applicant, where 0 signifies Single/Divorced/etc and 1 signifies Married.
- BankCustomer: Specifies whether the applicant is a bank customer, with 0 meaning they do not have a bank account and 1 meaning they do.
- Industry: Describes the job sector of the applicant's current or most recent employment.
- Ethnicity: Details the ethnicity of the applicant, including categories such as White and Black.
- YearsEmployed: The number of years the applicant has been employed.
- PriorDefault: Indicates whether the applicant has a prior default, with 0 for no prior defaults and 1 for a prior default.
- Employed: Shows the employment status of the applicant, where 0 means not employed and 1 means employed.
- CreditScore: Represents the applicant's credit score, which has been scaled.
- DriversLicense: Indicates if the applicant possesses a driving license, with 0 for no license and 1 for having a license.
- Citizen: Describes the applicant's citizenship status, categorised as ByBirth, ByOtherMeans, or Temporary.
- ZipCode: A five-digit numerical representation of the applicant's postcode.
- Income: The applicant's income, with this feature having been scaled.
- Approved: The target variable indicating the credit card approval status, where 0 means not approved and 1 means approved.
Distribution
The dataset is provided as a CSV file (
clean_dataset.csv) and has a size of 47.11 kB. It contains 16 distinct columns and consists of 690 valid records, with no missing values across any of the features.Usage
This dataset is ideal for:
- Developing and testing machine learning models for credit card approval prediction.
- Conducting data analysis to understand the key factors influencing credit decisions.
- Educational purposes in data science and machine learning, especially for beginners.
- Building predictive analytics solutions for financial institutions.
Coverage
The dataset's demographic scope covers various attributes including gender (female and male), age (ranging from 13.8 to 80.3 years), marital status, bank customer status, employment status, ethnicity (including White, Black, and other categories), and citizenship. While zip code information is included, specific geographic regions are not explicitly detailed. The time range of the data is not specified within the provided information. Notably, all features across the 690 records are valid, with no missing data points.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: To build and evaluate predictive models for credit approval.
- Academics and Researchers: For studies on credit risk, financial decision-making, and classification algorithms.
- Students and Beginners in Data Analysis: As a clean and well-documented dataset for learning and practice.
- Financial Analysts: To gain insights into factors affecting credit card approvals and potentially refine lending strategies.
Dataset Name Suggestions
- Credit Card Approval Prediction Dataset
- Clean Credit Approval Data
- Credit Card Applicant Approval Data
- Financial Credit Decision Dataset
Attributes
Original Data Source: Credit Card Approval Prediction Dataset
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