https://github.com/allwin107/loan-prediction-web-app
A Flask-based loan prediction web app using a Random Forest model to predict loan approval based on user input. It includes a clean, responsive UI, form validation, and real-time prediction display.
https://github.com/allwin107/loan-prediction-web-app
classification data-processing deployment flask loan-prediction machine-learning python random-forest-classifier scikit-learn web-application
Last synced: 7 months ago
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A Flask-based loan prediction web app using a Random Forest model to predict loan approval based on user input. It includes a clean, responsive UI, form validation, and real-time prediction display.
- Host: GitHub
- URL: https://github.com/allwin107/loan-prediction-web-app
- Owner: allwin107
- Created: 2025-03-15T14:25:54.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2025-03-15T15:04:22.000Z (7 months ago)
- Last Synced: 2025-03-15T16:20:13.587Z (7 months ago)
- Topics: classification, data-processing, deployment, flask, loan-prediction, machine-learning, python, random-forest-classifier, scikit-learn, web-application
- Language: Jupyter Notebook
- Homepage:
- Size: 3.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 🚀 Loan Prediction Web App
A Flask-based web application for predicting loan approval using a machine learning model.
## 📸 **Demo**
👉 
👉 
## 📂 **Project Structure**
├── loan_prediction_api
│ ├── templates
│ │ ├── index.html
│ │ ├── predict.html
│ ├── static
│ ├── app.py
│ ├── model.pkl
│ └── requirements.txt
├── README.md
└── .gitignore
## 🛠️ **Technologies Used**
- Python
- Flask
- Scikit-learn
- HTML + CSS
- Bootstrap (Optional)
## 🚀 **How to Run Locally**
1. **Clone the repository**
```bash
git clone https://github.com/allwin10/loan-prediction-web-app.git
cd loan_prediction_api
```
2. **Create a virtual environment**
```bash
python -m venv venv
source venv/bin/activate # For MacOS/Linux
venv\Scripts\activate # For Windows
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Run the app**
```bash
python app.py
```
5. Open your browser and go to:
👉 `http://127.0.0.1:5000`
---
## 📊 **Model Details**
- Model: `RandomForestClassifier`
- Input Features: 14
- Prediction: Approved or Rejected
---
## 🌟 **Features**
✅ Predicts loan approval based on user inputs
✅ Clean and responsive UI
✅ One-hot encoding for categorical data
✅ Flask-based backend
## 🤝 **Contributing**
1. Fork the repository
2. Create a new branch (`git checkout -b feature/your-feature`)
3. Commit your changes (`git commit -m 'Add new feature'`)
4. Push to the branch (`git push origin feature/your-feature`)
5. Open a Pull Request