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Credit Card Eligibility Factors Dataset
Finance & Banking Analytics
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About
This dataset is a collection of variables designed to help understand the factors that influence an individual's eligibility for a credit card. It includes a wide range of demographic, financial, and personal attributes that financial institutions typically consider when evaluating an individual's suitability for credit. Researchers, analysts, and financial institutions can utilise this data to gain insights into key eligibility factors and develop predictive models for credit assessment.
Columns
- ID: A unique identifier for each individual customer.
- Gender: The individual's gender, encoded as 0 for male and 1 for female. (Mean: 0.35, Std. Deviation: 0.48)
- Own_car: A binary feature indicating car ownership (0 for no, 1 for yes). (Mean: 0.37, Std. Deviation: 0.48)
- Own_property: A binary feature indicating property ownership (0 for no, 1 for yes). (Mean: 0.67, Std. Deviation: 0.47)
- Work_phone: A binary feature indicating if the individual has a work phone (0 for no, 1 for yes). (Mean: 0.22, Std. Deviation: 0.41)
- Phone: A binary feature indicating if the individual has a phone (0 for no, 1 for yes). (Mean: 0.29, Std. Deviation: 0.45)
- Email: A binary feature indicating if an email address has been provided (0 for no, 1 for yes). (Mean: 0.09, Std. Deviation: 0.28)
- Unemployed: A binary feature indicating unemployment status (0 for no, 1 for yes). (Mean: 0.17, Std. Deviation: 0.38)
- Num_children: The number of children the individual has. (Mean: 0.42, Std. Deviation: 0.77, Max: 19)
- Num_family: The total number of family members. (Mean: 2.18, Std. Deviation: 0.93, Max: 20)
- Account_length: The length of the individual's account with a bank or financial institution, in months. (Mean: 27.3, Std. Deviation: 16.6, Range: 0-60)
- Total_income: The total income of the individual. (Mean: 181k, Std. Deviation: 99.3k, Range: 27k-1.57m)
- Age: The age of the individual. (Mean: 43.8, Std. Deviation: 11.6, Range: 20.5-68.9)
- Years_employed: The number of years the individual has been employed. (Mean: 5.66, Std. Deviation: 6.34, Range: 0-43)
- Income_type: The type of income, with Working (51%) and Commercial associate (24%) being the most common.
- Education_type: The education level, with Secondary / secondary special (70%) and Higher education (25%) being the most frequent.
- Family_status: The family status, with Married (67%) and Single / not married (14%) being prevalent.
- Housing_type: The type of housing, with House / apartment (89%) as the most common.
- Occupation_type: The type of occupation, with Other (31%) and Laborers (18%) being common.
- Target: The target variable, indicating whether the individual is eligible for a credit card (0 for not eligible, 1 for eligible). (Mean: 0.13, Std. Deviation: 0.34)
Distribution
The dataset is typically provided as a CSV file (
dataset.csv) and consists of 9709 records (rows). It features 20 distinct columns that cover various attributes of individuals. For the profiled variables, there are no missing or mismatched entries, ensuring data quality for analysis. The data includes both numerical and categorical variables, with binary features for several personal attributes.Usage
This dataset is highly valuable for applications within the financial industry, including:
- Credit risk management: Assessing and mitigating credit risks.
- Customer relationship management: Enhancing customer understanding and engagement.
- Marketing analytics: Improving customer targeting and segmentation strategies.
- Predictive model development: Building models to automate credit assessment.
- Academic research and education: Exploring the intricate dynamics of credit card eligibility.
Coverage
The dataset focuses on individual demographic, financial, and personal attributes. It covers various aspects such as gender, age, number of children, family size, income level, employment status, education level, family status, housing type, and occupation type. The data provides insights into the backgrounds and circumstances of individuals as they relate to credit card eligibility. No specific geographic or time range is stated in the sources.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Financial Institutions: For making informed decisions, improving risk assessment, and refining customer targeting.
- Researchers and Analysts: To gain insights into credit card eligibility factors and develop predictive models.
- Students: For academic research and educational purposes to explore financial dynamics.
Dataset Name Suggestions
- Credit Card Eligibility Factors Dataset
- Individual Credit Suitability Data
- Financial Credit Assessment Attributes
- Credit Eligibility Determinants
Attributes
Original Data Source: Credit Card Eligibility Factors Dataset
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