AdityaaXD/Credit-Score-Classification

1---2language:3- en4size_categories:5- 100K<n<1M6task_categories:7- tabular-classification8task_ids:9- multi-class-classification10tags:11- credit-score12- finance13- banking14- tabular15- classification16pretty_name: Credit Score Classification Dataset17---18 19# 💳 Credit Score Classification Dataset20 21A comprehensive dataset for predicting customer credit scores into three categories: **Good**, **Standard**, and **Poor**.22 23## Dataset Description24 25This 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.26 27### Dataset Summary28 29| Property | Value |30|----------|-------|31| **Total Samples** | ~100,000+ |32| **Features** | 22 (17 numerical + 5 categorical) |33| **Target Classes** | Good, Standard, Poor |34| **Format** | CSV |35| **Language** | English |36 37## Dataset Structure38 39### Data Files40 41| File | Description | Size |42|------|-------------|------|43| `train.csv` | Training dataset | ~31 MB |44| `test.csv` | Test dataset | ~15 MB |45 46### Features47 48#### Numerical Features (17)49 50| Feature | Description | Data Type |51|---------|-------------|-----------|52| `Age` | Customer's age in years | Integer |53| `Annual_Income` | Yearly income | Float |54| `Monthly_Inhand_Salary` | Monthly take-home salary | Float |55| `Num_Bank_Accounts` | Number of bank accounts owned | Integer |56| `Num_Credit_Card` | Number of credit cards | Integer |57| `Interest_Rate` | Average interest rate on credit | Integer |58| `Num_of_Loan` | Number of active loans | Integer |59| `Delay_from_due_date` | Average payment delay in days | Integer |60| `Num_of_Delayed_Payment` | Count of delayed payments | Integer |61| `Changed_Credit_Limit` | Credit limit change percentage | Float |62| `Num_Credit_Inquiries` | Number of credit inquiries | Integer |63| `Outstanding_Debt` | Total outstanding debt amount | Float |64| `Credit_Utilization_Ratio` | Credit utilization percentage | Float |65| `Credit_History_Age_Months` | Length of credit history in months | Integer |66| `Total_EMI_per_month` | Monthly EMI payments | Float |67| `Amount_invested_monthly` | Monthly investment amount | Float |68| `Monthly_Balance` | Average monthly balance | Float |69 70#### Categorical Features (5)71 72| Feature | Description | Categories |73|---------|-------------|------------|74| `Month` | Month of record | January - December |75| `Occupation` | Employment type | Accountant, Architect, Developer, Doctor, Engineer, Entrepreneur, Journalist, Lawyer, Manager, Mechanic, Media_Manager, Musician, Scientist, Teacher, Writer |76| `Credit_Mix` | Types of credit accounts | Bad, Good, Standard |77| `Payment_of_Min_Amount` | Minimum payment behavior | Yes, No, NM |78| `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 |79 80#### Target Variable81 82| Feature | Description | Classes |83|---------|-------------|---------|84| `Credit_Score` | Credit score classification | Good, Standard, Poor |85 86## Dataset Statistics87 88### Class Distribution89 90| Class | Description |91|-------|-------------|92| **Good** | Customers with excellent credit profiles |93| **Standard** | Customers with average credit profiles |94| **Poor** | Customers with concerning credit profiles |95 96### Feature Statistics (Approximate)97 98| Feature | Min | Max | Mean |99|---------|-----|-----|------|100| Age | 14 | 100 | ~35 |101| Annual_Income | 0 | 500,000 | ~50,000 |102| Num_Bank_Accounts | 0 | 20 | ~5 |103| Credit_Utilization_Ratio | 0% | 100% | ~30% |104| Credit_History_Age_Months | 0 | 500 | ~200 |105 106## Usage107 108### Loading with Pandas109 110```python111import pandas as pd112 113# Load training data114train_df = pd.read_csv('train.csv')115print(f"Training samples: {len(train_df)}")116print(f"Features: {train_df.columns.tolist()}")117 118# Load test data119test_df = pd.read_csv('test.csv')120print(f"Test samples: {len(test_df)}")121```122 123### Basic Exploration124 125```python126# Check class distribution127print(train_df['Credit_Score'].value_counts())128 129# Check for missing values130print(train_df.isnull().sum())131 132# Statistical summary133print(train_df.describe())134```135 136## Data Preprocessing137 138The following preprocessing steps are recommended:139 1401. **Handle Missing Values**: Some columns may contain missing or placeholder values1412. **Clean Categorical Data**: Handle special characters in categorical columns1423. **Feature Scaling**: Apply StandardScaler to numerical features1434. **Encoding**: Use OneHotEncoder for categorical features, LabelEncoder for target144 145### Example Preprocessing146 147```python148from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder149import pandas as pd150 151# Numerical columns152numerical_cols = ['Age', 'Annual_Income', 'Monthly_Inhand_Salary', 153                  'Num_Bank_Accounts', 'Num_Credit_Card', 'Interest_Rate',154                  'Num_of_Loan', 'Delay_from_due_date', 'Num_of_Delayed_Payment',155                  'Changed_Credit_Limit', 'Num_Credit_Inquiries', 'Outstanding_Debt',156                  'Credit_Utilization_Ratio', 'Credit_History_Age_Months',157                  'Total_EMI_per_month', 'Amount_invested_monthly', 'Monthly_Balance']158 159# Categorical columns160categorical_cols = ['Month', 'Occupation', 'Credit_Mix', 161                    'Payment_of_Min_Amount', 'Payment_Behaviour']162 163# Scale numerical features164scaler = StandardScaler()165X_numerical = scaler.fit_transform(train_df[numerical_cols])166 167# Encode target168label_encoder = LabelEncoder()169y = label_encoder.fit_transform(train_df['Credit_Score'])170```171 172## Considerations for Using the Data173 174### Data Quality Issues175 176- Some columns may contain placeholder values (e.g., `_`, `________`, `!@9#%8`)177- Credit history age may need conversion from text format178- Some numerical columns may have outliers179 180### Ethical Considerations181 182⚠️ **Important**: When using this data for credit scoring models:183 184- Be aware of potential biases in the data185- Ensure compliance with local financial regulations186- Credit decisions should not be based solely on automated predictions187- Provide transparency and explanations for credit decisions188 189### Recommended Cleaning Steps190 191```python192# Example: Handle placeholder values193placeholders = ['_', '________', '!@9#%8', 'NM']194for col in categorical_cols:195    train_df[col] = train_df[col].replace(placeholders, 'Unknown')196```197 198## Related Models199 200- **Model**: [AdityaaXD/credit-score-classifier](https://huggingface.co/AdityaaXD/credit-score-classifier)201- **GitHub**: [Credit-Score-Classification](https://github.com/ADITYA-tp01/Credit-Score-Clasification)202 203## Citation204 205```bibtex206@dataset{credit-score-dataset,207  author = {Aditya},208  title = {Credit Score Classification Dataset},209  year = {2026},210  publisher = {Hugging Face},211  url = {https://huggingface.co/datasets/AdityaaXD/credit-score-dataset}212}213```214 215## Contact216 217- **Hugging Face**: [@AdityaaXD](https://huggingface.co/AdityaaXD)218- **GitHub**: [@ADITYA-tp01](https://github.com/ADITYA-tp01)219