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README.md

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+---
+language:
+- en
+tags:
+- tabular-classification
+- credit-score
+- random-forest
+- sklearn
+- finance
+- banking
+pipeline_tag: tabular-classification
+library_name: sklearn
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: credit-score-classifier
+  results:
+  - task:
+      type: tabular-classification
+      name: Credit Score Classification
+    metrics:
+    - type: accuracy
+      value: 0.80
+      name: Accuracy
+---
+
+# 💳 Credit Score Classifier
+
+A **Random Forest Classifier** trained to predict customer credit scores into three categories: **Good**, **Standard**, and **Poor**.
+
+## Model Description
+
+This model analyzes customer financial data and behavioral patterns to classify their credit worthiness. It was trained on a comprehensive dataset containing financial metrics, payment history, and credit utilization patterns.
+
+### Model Details
+
+| Property | Value |
+|----------|-------|
+| **Model Type** | Random Forest Classifier |
+| **Framework** | Scikit-learn |
+| **Number of Trees** | 100 |
+| **Target Classes** | Good, Standard, Poor |
+| **Input Features** | 17 numerical + 5 categorical |
+
+## Intended Use
+
+### Primary Use Cases
+- **Credit Risk Assessment**: Evaluate creditworthiness of loan applicants
+- **Financial Services**: Automate preliminary credit screening
+- **Banking Applications**: Support credit limit decisions
+
+### Out-of-Scope Use
+- This model should not be the sole decision-maker for credit approvals
+- Not intended for use without human oversight
+- Should not be used for discriminatory purposes
+
+## How to Use
+
+### Installation
+
+```bash
+pip install huggingface_hub scikit-learn pandas numpy
+```
+
+### Loading the Model
+
+```python
+from huggingface_hub import hf_hub_download
+import pickle
+
+# Download model files
+model_path = hf_hub_download(repo_id="AdityaaXD/credit-score-classifier", filename="models/final_model.pkl")
+scaler_path = hf_hub_download(repo_id="AdityaaXD/credit-score-classifier", filename="models/scaler.pkl")
+label_encoder_path = hf_hub_download(repo_id="AdityaaXD/credit-score-classifier", filename="models/label_encoder.pkl")
+feature_info_path = hf_hub_download(repo_id="AdityaaXD/credit-score-classifier", filename="models/feature_info.pkl")
+
+# Load the model
+with open(model_path, "rb") as f:
+    model = pickle.load(f)
+
+with open(scaler_path, "rb") as f:
+    scaler = pickle.load(f)
+
+with open(label_encoder_path, "rb") as f:
+    label_encoder = pickle.load(f)
+```
+
+### Making Predictions
+
+```python
+import pandas as pd
+import numpy as np
+
+# Example: Prepare your input data
+numerical_features = {
+    'Age': 30,
+    'Annual_Income': 50000,
+    'Monthly_Inhand_Salary': 4000,
+    'Num_Bank_Accounts': 4,
+    'Num_Credit_Card': 3,
+    'Interest_Rate': 12,
+    'Num_of_Loan': 2,
+    'Delay_from_due_date': 5,
+    'Num_of_Delayed_Payment': 3,
+    'Changed_Credit_Limit': 8.0,
+    'Num_Credit_Inquiries': 4,
+    'Outstanding_Debt': 1200,
+    'Credit_Utilization_Ratio': 28.5,
+    'Credit_History_Age_Months': 180,
+    'Total_EMI_per_month': 150,
+    'Amount_invested_monthly': 200,
+    'Monthly_Balance': 500
+}
+
+# Scale numerical features
+num_df = pd.DataFrame([numerical_features])
+scaled_features = scaler.transform(num_df)
+
+# Make prediction (note: categorical features need one-hot encoding)
+prediction = model.predict(scaled_features)
+predicted_class = label_encoder.inverse_transform(prediction)
+print(f"Predicted Credit Score: {predicted_class[0]}")
+```
+
+## Training Data
+
+The model was trained on a credit score dataset containing:
+
+| Feature Type | Count | Examples |
+|--------------|-------|----------|
+| **Numerical** | 17 | Age, Annual Income, Outstanding Debt, Credit Utilization |
+| **Categorical** | 5 | Occupation, Credit Mix, Payment Behavior |
+
+### Input Features
+
+#### Numerical Features
+- `Age` - Customer's age
+- `Annual_Income` - Yearly income
+- `Monthly_Inhand_Salary` - Monthly take-home salary
+- `Num_Bank_Accounts` - Number of bank accounts
+- `Num_Credit_Card` - Number of credit cards
+- `Interest_Rate` - Average interest rate
+- `Num_of_Loan` - Number of active loans
+- `Delay_from_due_date` - Average payment delay (days)
+- `Num_of_Delayed_Payment` - Count of delayed payments
+- `Changed_Credit_Limit` - Credit limit changes
+- `Num_Credit_Inquiries` - Number of credit inquiries
+- `Outstanding_Debt` - Total outstanding debt
+- `Credit_Utilization_Ratio` - Credit utilization percentage
+- `Credit_History_Age_Months` - Credit history length
+- `Total_EMI_per_month` - Monthly EMI payments
+- `Amount_invested_monthly` - Monthly investments
+- `Monthly_Balance` - Average monthly balance
+
+#### Categorical Features
+- `Month` - Month of record
+- `Occupation` - Employment type
+- `Credit_Mix` - Types of credit accounts
+- `Payment_of_Min_Amount` - Minimum payment behavior
+- `Payment_Behaviour` - Spending patterns
+
+## Model Files
+
+| File | Description |
+|------|-------------|
+| `models/final_model.pkl` | Trained Random Forest model |
+| `models/scaler.pkl` | StandardScaler for numerical features |
+| `models/label_encoder.pkl` | LabelEncoder for target classes |
+| `models/feature_info.pkl` | Feature metadata and column names |
+| `models/onehot_encoder.pkl` | OneHotEncoder for categorical features |
+
+## Limitations
+
+- **Data Bias**: Model performance depends on training data quality and may not generalize to all populations
+- **Feature Availability**: Requires all 17 numerical and 5 categorical features for accurate predictions
+- **Temporal Drift**: Financial patterns change over time; periodic retraining recommended
+- **Geographic Scope**: Trained on specific regional data; may need adaptation for other regions
+
+## Ethical Considerations
+
+⚠️ **Important**: This model is intended as a decision-support tool, not a replacement for human judgment.
+
+- Always combine model predictions with human review
+- Be aware of potential biases in credit scoring
+- Ensure compliance with local financial regulations
+- Provide explanations for credit decisions when required by law
+
+## Demo Application
+
+Try the interactive Streamlit demo: [Credit Score Classifier App](https://github.com/ADITYA-tp01/Credit-Score-Clasification)
+
+## Citation
+
+```bibtex
+@misc{credit-score-classifier,
+  author = {Aditya},
+  title = {Credit Score Classification using Random Forest},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/AdityaaXD/credit-score-classifier}
+}
+```
+
+## Contact
+
+- **Hugging Face**: [@AdityaaXD](https://huggingface.co/AdityaaXD)
+- **GitHub**: [@ADITYA-tp01](https://github.com/ADITYA-tp01)