AdityaaXD/Credit-Score-Classification

💳 Credit Score Classification Dataset

A comprehensive dataset for predicting customer credit scores into three categories: Good, Standard, and Poor.

Dataset Description

This dataset contains customer financial information and behavioral patterns used for credit score classification. It includes various features related to credit history, payment behavior, and financial metrics.

Dataset Summary

Property	Value
Total Samples	~100,000+
Features	22 (17 numerical + 5 categorical)
Target Classes	Good, Standard, Poor
Format	CSV
Language	English

Dataset Structure

Data Files

File	Description	Size
train.csv	Training dataset	~31 MB
test.csv	Test dataset	~15 MB

Features

Numerical Features (17)

Feature	Description	Data Type
Age	Customer's age in years	Integer
Annual_Income	Yearly income	Float
Monthly_Inhand_Salary	Monthly take-home salary	Float
Num_Bank_Accounts	Number of bank accounts owned	Integer
Num_Credit_Card	Number of credit cards	Integer
Interest_Rate	Average interest rate on credit	Integer
Num_of_Loan	Number of active loans	Integer
Delay_from_due_date	Average payment delay in days	Integer
Num_of_Delayed_Payment	Count of delayed payments	Integer
Changed_Credit_Limit	Credit limit change percentage	Float
Num_Credit_Inquiries	Number of credit inquiries	Integer
Outstanding_Debt	Total outstanding debt amount	Float
Credit_Utilization_Ratio	Credit utilization percentage	Float
Credit_History_Age_Months	Length of credit history in months	Integer
Total_EMI_per_month	Monthly EMI payments	Float
Amount_invested_monthly	Monthly investment amount	Float
Monthly_Balance	Average monthly balance	Float

Categorical Features (5)

Feature	Description	Categories
Month	Month of record	January - December
Occupation	Employment type	Accountant, Architect, Developer, Doctor, Engineer, Entrepreneur, Journalist, Lawyer, Manager, Mechanic, Media_Manager, Musician, Scientist, Teacher, Writer
Credit_Mix	Types of credit accounts	Bad, Good, Standard
Payment_of_Min_Amount	Minimum payment behavior	Yes, No, NM
Payment_Behaviour	Spending patterns	High_spent_Large_value_payments, High_spent_Medium_value_payments, High_spent_Small_value_payments, Low_spent_Large_value_payments, Low_spent_Medium_value_payments, Low_spent_Small_value_payments

Target Variable

Feature	Description	Classes
Credit_Score	Credit score classification	Good, Standard, Poor

Dataset Statistics

Class Distribution

Class	Description
Good	Customers with excellent credit profiles
Standard	Customers with average credit profiles
Poor	Customers with concerning credit profiles

Feature Statistics (Approximate)

Feature	Min	Max	Mean
Age	14	100	~35
Annual_Income	0	500,000	~50,000
Num_Bank_Accounts	0	20	~5
Credit_Utilization_Ratio	0%	100%	~30%
Credit_History_Age_Months	0	500	~200

Usage

Loading with Pandas

import pandas as pd

# Load training data
train_df = pd.read_csv('train.csv')
print(f"Training samples: {len(train_df)}")
print(f"Features: {train_df.columns.tolist()}")

# Load test data
test_df = pd.read_csv('test.csv')
print(f"Test samples: {len(test_df)}")

Basic Exploration

# Check class distribution
print(train_df['Credit_Score'].value_counts())

# Check for missing values
print(train_df.isnull().sum())

# Statistical summary
print(train_df.describe())

Data Preprocessing

The following preprocessing steps are recommended:

1. Handle Missing Values: Some columns may contain missing or placeholder values
2. Clean Categorical Data: Handle special characters in categorical columns
3. Feature Scaling: Apply StandardScaler to numerical features
4. Encoding: Use OneHotEncoder for categorical features, LabelEncoder for target

Example Preprocessing

from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
import pandas as pd

# Numerical columns
numerical_cols = ['Age', 'Annual_Income', 'Monthly_Inhand_Salary', 
                  'Num_Bank_Accounts', 'Num_Credit_Card', 'Interest_Rate',
                  'Num_of_Loan', 'Delay_from_due_date', 'Num_of_Delayed_Payment',
                  'Changed_Credit_Limit', 'Num_Credit_Inquiries', 'Outstanding_Debt',
                  'Credit_Utilization_Ratio', 'Credit_History_Age_Months',
                  'Total_EMI_per_month', 'Amount_invested_monthly', 'Monthly_Balance']

# Categorical columns
categorical_cols = ['Month', 'Occupation', 'Credit_Mix', 
                    'Payment_of_Min_Amount', 'Payment_Behaviour']

# Scale numerical features
scaler = StandardScaler()
X_numerical = scaler.fit_transform(train_df[numerical_cols])

# Encode target
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(train_df['Credit_Score'])

Considerations for Using the Data

Data Quality Issues

• Some columns may contain placeholder values (e.g., _, ________, !@9#%8)
• Credit history age may need conversion from text format
• Some numerical columns may have outliers

Ethical Considerations

⚠️ Important: When using this data for credit scoring models:

• Be aware of potential biases in the data
• Ensure compliance with local financial regulations
• Credit decisions should not be based solely on automated predictions
• Provide transparency and explanations for credit decisions

Recommended Cleaning Steps

# Example: Handle placeholder values
placeholders = ['_', '________', '!@9#%8', 'NM']
for col in categorical_cols:
    train_df[col] = train_df[col].replace(placeholders, 'Unknown')

Related Models

• Model: AdityaaXD/credit-score-classifier
• GitHub: Credit-Score-Classification

Citation

@dataset{credit-score-dataset,
  author = {Aditya},
  title = {Credit Score Classification Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/AdityaaXD/credit-score-dataset}
}

Contact

• Hugging Face: @AdityaaXD
• GitHub: @ADITYA-tp01