# Credit Risk Classifier — Data

来源：https://www.kaggle.com/competitions/credit-risk-classifier/data

采集时间：2026-10-20T09:31:46Z

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below.

Reference to the original data source: [https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)](url)

you can find a sample visualization from here: [https://www.kaggle.com/kabure/predicting-credit-risk-model-pipeline](url)

The dataset has been split into a 80/20 training/test split. The files are csv. Please perform data analysis on the training file to predict the classification as good or bad credit risks. Try to get an accuracy score above .68.

**Age** (numeric)
**Sex** (text: male, female)
**Job** (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)
**Housing** (text: own, rent, or free)
**Saving accounts** (text - little, moderate, quite rich, rich)
**Checking account** (numeric, in DM - Deutsch Mark)
**Credit amount** (numeric, in DM)
**Duration** (numeric, in month)
**Purpose**(text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others
**Risk** (Value target - Good or Bad Risk)

## Files

*   **german_credit_data_training_set.csv** - the training data set 
*   **german_credit_data_test_set.csv** - the test data set 
*   **sample_submission.csv** - a sample submission file in the correct format 
## Columns

*   `example_id` - definition of example_id
*   `feature_1` - definition of feature_1
*   etc.
*   `Age` (numeric)
*   `Sex` (text: male, female)
*   `Job` (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)
*   `Housing` (text: own, rent, or free)
*   `Saving accounts` (text - little, moderate, quite rich, rich)
*   `Checking account` (numeric, in DM - Deutsch Mark)
*   `Credit amount` (numeric, in DM)
*   `Duration` (numeric, in month)
*   `Purpose` (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others
*   `Risk` (Value target - Good or Bad Risk)