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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+… See the full description on the dataset page: https://huggingface.co/datasets/AdityaaXD/Credit-Score-Classification.

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💳 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	HighspentLargevaluepayments, HighspentMediumvaluepayments, HighspentSmallvaluepayments, LowspentLargevaluepayments, LowspentMediumvaluepayments, LowspentSmallvaluepayments

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
NumBankAccounts	0	20	~5
CreditUtilizationRatio	0%	100%	~30%
CreditHistoryAge_Months	0	500	~200

Usage

Loading with Pandas

python

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

python

# 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

python

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

python

# 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

bibtex

@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