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Cleaned Credit Risk Data
Finance & Banking Analytics
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Free
About
This dataset provides cleaned information intended for credit card classification, specifically to predict whether credit card users represent a high or low risk. It offers a structured foundation for developing models that assess client creditworthiness and for analytical studies on factors influencing financial risk.
Columns
- ID: Unique identifier for each client.
- Gender: Indicates the client's gender (1=Male, 0=Female).
- Own_car: Specifies if the client owns a car (1=Yes, 0=No).
- Own_property: Specifies if the client owns property (1=Yes, 0=No).
- Work_phone: Indicates if the client possesses a work phone (1=Yes, 0=No).
- Phone: Indicates if the client possesses a phone (1=Yes, 0=No).
- Email: Specifies if the client has an email address (1=Yes, 0=No).
- Unemployed: Indicates if the client is unemployed (1=Yes, 0=No).
- Num_children: The number of children the client has.
- Num_family: The total number of family members in the client's household.
- Account_length: The duration, in months, that the credit card has been owned by the client.
- Total_income: The client's total income, expressed in Chinese yuan.
- Age: The client's age in years.
- Years_employed: The total number of years the client has been employed.
- Income_type: Categorical data describing the client's income source (e.g., Working, Commercial associate).
- Education_type: Categorical data describing the client's educational background (e.g., Secondary / secondary special, Higher education).
- Family_status: Categorical data describing the client's family status (e.g., Married, Single / not married).
- Housing_type: Categorical data describing the client's housing situation (e.g., House / apartment, With parents).
- Occupation_type: Categorical data describing the client's occupation type (e.g., Laborers, Other).
- Target: The classification target, indicating whether the client is high risk (1) or low risk (0).
Distribution
This dataset is provided as a cleaned CSV file,
clean_data.csv, with a file size of 1.46 MB. It contains 20 columns and features 9709 valid records. There are no mismatched or missing values within these records, ensuring data quality.Usage
This dataset is ideal for building and testing machine learning models aimed at predicting credit card risk. It can be utilised for credit card classification tasks, analysing the various factors that contribute to a client's risk profile, and for developing automated risk assessment systems in financial services.
Coverage
The dataset includes demographic information such as gender, age, family status, number of children, and family members. It also covers financial aspects like total income (in Chinese yuan), employment status, years employed, and credit account length. Education, housing, and occupation types are also detailed. Specific geographic regions beyond the currency and a precise time range are not explicitly stated, though account length provides temporal context for individual accounts.
License
CC0: Public Domain
Who Can Use It
- Data scientists developing predictive models for financial risk assessment.
- Financial institutions for enhancing internal credit scoring systems and decision-making processes.
- Researchers investigating the socio-economic determinants of credit behaviour.
- Academics and students for educational projects in data science, machine learning, and finance.
Dataset Name Suggestions
- Credit Client Risk Predictor
- Financial Risk Assessment Data
- Creditworthiness Prediction Dataset
- User Credit Risk Profiles
- Cleaned Credit Risk Data
Attributes
Original Data Source:Cleaned Credit Risk Data
QUALITY