Loan Credit Risk Classifier

A machine learning project that predicts loan approval risk using applicant financial and demographic information.

Project Overview

This project uses a Random Forest Classifier to analyze loan applicant information and predict whether a loan application is likely to be approved.

The project includes:

• Data preprocessing
• Missing-value handling
• Categorical feature encoding
• Random Forest classification
• Model evaluation
• Prediction system
• Interactive Streamlit web application

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Machine Learning Model

Algorithm: Random Forest Classifier

The model is trained using the following applicant information:

• Gender
• Marital Status
• Dependents
• Education
• Self Employment
• Applicant Income
• Coapplicant Income
• Loan Amount
• Loan Amount Term
• Credit History
• Property Area

Loan_ID is removed because it does not provide useful information for prediction.

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Model Performance

The current model achieved:

Test Accuracy: 82.11%

Classification performance:

Class	Precision	Recall	F1-Score
N	0.75	0.63	0.69
Y	0.85	0.91	0.88

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Joblib
• Streamlit
• Git & GitHub

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Installation & Usage

Clone Repository

git clone https://github.com/yesajayyy/Loan-Credit-Risk-Classifier.git

Navigate to Project Folder

cd Loan-Credit-Risk-Classifier

Install Required Libraries

pip install -r requirements.txt

Run the Application

streamlit run app/app.py

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🌐 Live Demo

The Loan Credit Risk Classifier application is deployed and available online.

Try the live application here:

🔗 https://loan-credit-risk-classifier-zpkqtcmujvjongwjgdb72t.streamlit.app/

The model is trained using loan applicant data containing financial and demographic information.

Features include:

• Applicant details
• Income information
• Loan details
• Credit history
• Property information

The dataset is used to classify loan applications into different risk categories.

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📁 Project Structure

Loan-Credit-Risk-Classifier/
│
├── app/
│   └── app.py
│
├── data/
│   └── train.csv
│
├── models/
│   └── loan_risk_model.pkl
│
├── src/
│   ├── train.py
│   └── predict.py
│
├── images/
│   └── app_screenshot.png
│
├── notebooks/
│
├── .gitignore
├── requirements.txt
└── README.md

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Future Improvements

• Deploy the application using Streamlit Cloud
• Add advanced machine learning algorithms
• Improve model accuracy using hyperparameter tuning
• Implement Explainable AI techniques like SHAP
• Add more financial features for better prediction

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Author

Guguloth Ajay

Electronics and Instrumentation Engineering Student

GitHub: https://github.com/yesajayyy