🏦 Bank Loan Approval Prediction

📌 Project Overview

This project uses machine learning to predict whether a bank loan should be approved based on applicant details. It helps banks automate and speed up the loan approval process while reducing manual errors.

📂 Dataset

• Dataset Used: Loan Prediction Dataset (from Kaggle or similar)
• Target Variable: Loan_Status (Approved / Not Approved)
• Features: loan_id, no_of_dependents, education, self_employed, income_annum, loan_amount, loan_term, cibil_score, residential_assets_value, commercial_assets_value, luxury_assets_value, bank_asset_value

📜 Features

• Data cleaning and preprocessing
• Handling missing values and encoding categorical data
• Feature scaling
• ML models:Random Forest
• Model evaluation using accuracy, precision, recall, confusion matrix
• Prediction system using user inputs

🛠️ Technologies Used

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Streamlit (for deployment)

🚀 Installation & Usage

1. Clone the repository:

   git clone https://github.com/sivaramaraju2124/Bank-Loan-Prediction.git
   cd Bank-Loan-Prediction

2. Install dependencies:

    pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

📊 Model Performance

Model Accuracy

• Random Forest 98%

📸 Live Link(https://bank-approval-prediction.streamlit.app/)

🤝 Contributing

Contributions are welcome! Feel free to fork this repo and submit a pull request.

📜 License

This project is licensed under a GNU Public License.

✨ Author

Developed by G Swami Siva Rama Raju