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README.md

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+---
+language:
+- en
+size_categories:
+- 100K<n<1M
+task_categories:
+- tabular-classification
+task_ids:
+- multi-class-classification
+tags:
+- credit-score
+- finance
+- banking
+- tabular
+- classification
+pretty_name: Credit Score Classification Dataset
+---
+
+# 💳 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
+
+```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](https://huggingface.co/AdityaaXD/credit-score-classifier)
+- **GitHub**: [Credit-Score-Classification](https://github.com/ADITYA-tp01/Credit-Score-Clasification)
+
+## 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](https://huggingface.co/AdityaaXD)
+- **GitHub**: [@ADITYA-tp01](https://github.com/ADITYA-tp01)