Dark Mode
Global Credit Demographics Dataset
Finance & Banking Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides insights into the global demographics and financial behaviours of over 100 individuals, primarily for the purpose of credit score classification. It offers a valuable resource for understanding the diverse factors that influence an individual's credit standing. The dataset aims to facilitate analysis of how age, gender, income, education, marital status, number of children, and home ownership correlate with credit scores.
Columns
The dataset contains 8 distinct columns, each detailing a specific attribute:
- Age: Represents the age of the individual. Values range from 25 to 53, with a mean age of 38 and a standard deviation of 8.45.
- Gender: Indicates the individual's gender, categorised as Male or Female. Females constitute 52% of the sample, while Males account for 48%.
- Income: Reflects the individual's salary. Incomes range from £25,000 to £163,000, with a mean income of £83,800 and a standard deviation of £32,400.
- Education: Describes the highest level of education attained. Categories include Bachelor's Degree (26%), Master's Degree (22%), and Other (52%).
- Marital Status: Denotes the individual's marital status. Married individuals make up 53% of the sample, with Single individuals making up 47%.
- Number of Children: Shows the count of children an individual has. Values range from 0 to 3, with a mean of 0.65 and a standard deviation of 0.88.
- Home Ownership: Indicates whether the individual owns or rents their home. 68% of the sample own their homes, while 32% rent.
- Credit Score: The target variable, classifying the individual's credit score. Categories include High (69%), Average (22%), and Other (9%).
Distribution
The dataset is provided in a CSV format, specifically named "Credit Score Classification Dataset.csv". It contains information for 164 valid records across all 8 columns. The file size is 9 KB.
Usage
This dataset is ideal for:
- Developing and evaluating machine learning models for credit score prediction.
- Conducting demographic analysis to understand socio-economic patterns related to creditworthiness.
- Researching the impact of various personal and financial attributes on credit scores.
- Educational purposes in finance, statistics, and data science courses.
Coverage
The dataset covers a sample of over 100 people across the world, offering a global demographic scope. It includes individuals with ages ranging from 25 to 53 years, diverse income brackets, and varying educational backgrounds. Demographic attributes such as gender, marital status, number of children, and home ownership are captured. The data does not specify a particular time range for collection, indicating it represents a snapshot.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is suitable for:
- Data Scientists and Machine Learning Engineers: For building and testing credit scoring algorithms.
- Financial Analysts and Institutions: To gain insights into factors affecting credit risk and to inform lending policies.
- Academic Researchers: For studies on consumer finance, economic behaviour, and demographic influences on financial outcomes.
- Students: As a practical resource for learning data analysis, statistical modelling, and machine learning applications in finance.
Dataset Name Suggestions
- Global Credit Demographics Dataset
- Credit Score Classification Factors
- Financial Behaviour and Credit Scores
- Demographic Creditworthiness Data
Attributes
Original Data Source: Global Credit Demographics Dataset
QUALITY