🏠 House Price Prediction Web Application

A full-stack web application that predicts house prices using a Machine Learning model integrated into a web interface. This project combines Django (backend) and Linear Regression (ML model) to provide users with accurate housing price predictions based on input features.

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🚀 Features

• 📊 Predict house prices using a trained Linear Regression model
• 🌐 Interactive web interface using HTML, CSS, and JavaScript
• 🔐 User authentication (Login & Register system)
• 📁 Organized project structure with Django apps
• 📈 Real dataset (USA_Housing.csv) for training and predictions
• 📦 Modular and scalable codebase

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🛠️ Tech Stack

Frontend:

• HTML5
• CSS3
• JavaScript

Backend:

• Python
• Django

Machine Learning:

• Linear Regression
• Pandas, NumPy, Scikit-learn

Database:

• SQLite3

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📂 Project Structure

BuynSellHouse/
│
├── BuynSellHouse/        # Main Django project settings
│   ├── __init__.py
│   ├── asgi.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
│
├── core/                 # Main application logic
│   ├── migrations/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── forms.py
│   ├── models.py
│   ├── regression_model.py   # ML model implementation
│   ├── views.py
│   ├── urls.py
│   ├── tests.py
│   └── USA_Housing.csv       # Dataset
│
├── templates/enroll/     # HTML templates
│   ├── base.html
│   ├── home.html
│   ├── login.html
│   ├── register.html
│   ├── prediction.html
│   ├── sell.html
│   ├── about.html
│   ├── contact.html
│   ├── header.html
│   └── footer.html
│
├── static/enroll/        # Static files
│   ├── css/
│   ├── js/
│   └── img/
│
├── media/                # Uploaded media files
├── db.sqlite3            # Database
├── manage.py             # Django management script
└── text.md               # Additional notes

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

2️⃣ Create virtual environment

python -m venv venv
venv\Scripts\activate   # For Windows

3️⃣ Install dependencies

pip install -r requirements.txt

(If requirements.txt is not available, install manually: Django, pandas, numpy, scikit-learn)

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4️⃣ Run migrations

python manage.py migrate

5️⃣ Start the server

python manage.py runserver

6️⃣ Open in browser

http://127.0.0.1:8000/

How It Works

1. User enters property details (area, location factors, etc.)
2. Data is sent to backend via Django views
3. regression_model.py processes the input
4. ML model predicts the house price
5. Result is displayed on the UI

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Author

Varsha Rajput

• Computer Science Student
• Passionate about Web Development & Machine Learning

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Acknowledgment

This project is built as part of learning and applying:

• Machine Learning concepts
• Django web development
• Full-stack integration

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Note

This project is intended for educational purposes and demonstrates the integration of ML models into web applications.

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💡 If you like this project, don’t forget to star the repository!