Credit-Risk-Modelling-System_final

📊 Credit Risk Modelling System

📌 Project Overview

This project provides an advanced Credit Risk Prediction System using machine learning models.
It evaluates the risk level of loan applicants based on financial history, behavioral patterns, and loan attributes.

💡 Key Features: ✅ Uses XGBoost Classifier for predictions
✅ Preprocesses & normalizes data for better accuracy
✅ Implements custom credit scoring & risk assessment
✅ Handles categorical & numerical financial data effectively

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⚙️ Installation Guide

Step 1: Clone the Repository

git clone https://github.com/Vedant4102004/Credit-Risk-Modelling-System.git
cd Credit-Risk-Modelling-System

Step 2: Install Dependencies

Make sure you have all required Python libraries installed:

pip install -r requirements.txt

Dependencies Include:

• NumPy, Pandas (Data manipulation)
• Scikit-learn (Feature processing)
• XGBoost (Machine learning model)
• Joblib (Model saving/loading)

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🏗 Model Training & Preprocessing

Step 3: Data Preprocessing

The dataset undergoes feature engineering & preprocessing:
✅ Scaling numerical features using MinMaxScaler
✅ Encoding categorical variables for compatibility
✅ Handling missing values appropriately

Example preprocessing step:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)  # Scale features for training

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Step 4: Training the Model

Train the XGBoost Classifier with encoded data:

from xgboost import XGBClassifier

# Define & train the model
model = XGBClassifier()
model.fit(X_train_encoded, y_train)  # Ensure X_train_encoded & y_train are defined

Save the trained model for future use:

from joblib import dump
dump(model, "credit_risk_model.joblib")

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🏦 Prediction System

Step 5: Credit Risk Evaluation

The model predicts default probability & credit score using financial inputs.
📊 Credit Rating System:

Credit Score	Risk Level
300 - 500	Poor
500 - 650	Average
650 - 750	Good
750 - 900	Excellent

Example Prediction Code

import joblib
import numpy as np

# Load trained model
model = joblib.load("credit_risk_model.joblib")

# Sample customer data (modify as needed)
customer_data = np.array([[30, 1200000, 2500000, 24, 15, 25, 40, 2]])

# Make predictions
credit_risk = model.predict(customer_data)
print("Credit Risk Prediction:", "High Risk" if credit_risk[0] == 1 else "Low Risk")

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🚀 Next Steps

✅ Improve feature engineering with deeper financial insights
✅ Deploy the model for real-world credit evaluation
✅ Optimize parameters for higher accuracy

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🤝 Contributing

If you'd like to improve this project:
🔹 Fork the repository
🔹 Work on enhancements
🔹 Submit a pull request 🚀

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