Price Prediction and Recommendation System

A full-stack web application for property price prediction and intelligent property recommendations. This system combines machine learning capabilities with a modern web interface to help users predict property prices and find suitable properties based on their preferences.

Features

• Property Price Prediction: Predict property prices based on various features using machine learning models
• Property Recommendations: Get personalized property recommendations based on user preferences
• Modern UI: Responsive and intuitive user interface built with Next.js and React
• REST API: Complete backend API for prediction and recommendation services
• Real-time Processing: Fast prediction and recommendation generation

Technology Stack

Frontend

• Next.js 14: React framework for production
• TypeScript: Type-safe development
• Tailwind CSS: Utility-first CSS framework
• React Components: Custom UI component library
• Shadcn UI: High-quality React components

Backend

• Python 3.x: Core programming language
• FastAPI: Modern, fast web framework for building APIs
• Machine Learning: Models for price prediction and recommendations
• Pandas/Numpy: Data processing and manipulation

Project Structure

.
├── app/                    # Next.js app directory
│   ├── layout.tsx         # Root layout component
│   ├── page.tsx           # Home page
│   └── globals.css        # Global styles
├── components/            # React components
│   ├── ui/               # Reusable UI components
│   ├── navbar.tsx        # Navigation bar
│   ├── hero.tsx          # Hero section
│   ├── about.tsx         # About section
│   ├── footer.tsx        # Footer component
│   ├── price-prediction.tsx      # Price prediction component
│   └── property-recommendation.tsx # Recommendations component
├── backend/              # Python backend
│   ├── api/
│   │   ├── main.py      # FastAPI application entry point
│   │   ├── recommendation.py # Recommendation logic
│   │   └── __init__.py
│   ├── datasets/
│   │   └── appartments.csv # Property dataset
│   ├── models/          # Machine learning models
│   ├── services/        # Business logic services
│   └── requirements.txt  # Python dependencies
├── hooks/               # Custom React hooks
├── lib/                 # Utility functions
│   ├── api.ts          # API client functions
│   └── utils.ts        # Helper utilities
├── public/             # Static assets
└── styles/             # Global stylesheets

Installation

Prerequisites

• Node.js 18+ and npm/pnpm
• Python 3.8+
• Git

Frontend Setup

1. Navigate to the project root:

cd Price-Prediction-and-Recommendation-System

2. Install dependencies:

pnpm install
# or
npm install

3. Create a .env.local file for environment variables (if needed):

NEXT_PUBLIC_API_URL=http://localhost:8000

Backend Setup

1. Navigate to the backend directory:

cd backend

2. Create a Python virtual environment:

python -m venv venv
source venv/Scripts/activate  # On Windows
# or
source venv/bin/activate  # On macOS/Linux

3. Install Python dependencies:

pip install -r requirements.txt

Running the Application

Development Mode

Terminal 1 - Frontend (Next.js):

pnpm dev
# or
npm run dev

The frontend will be available at http://localhost:3000

Terminal 2 - Backend (FastAPI):

cd backend
python -m api.main
# or
uvicorn api.main:app --reload

The backend API will be available at http://localhost:8000

Production Build

Frontend:

pnpm build
pnpm start

Backend:

cd backend
uvicorn api.main:app --host 0.0.0.0 --port 8000

API Endpoints

Price Prediction

• POST /api/predict - Predict property price based on features
  • Request: Property features (location, size, bedrooms, etc.)
  • Response: Predicted price

Recommendations

• POST /api/recommend - Get property recommendations
  • Request: User preferences and criteria
  • Response: List of recommended properties

Configuration

Environment Variables

Create .env.local in the root directory for frontend configuration:

NEXT_PUBLIC_API_URL=http://localhost:8000

Key Components

Frontend Components

• Price Prediction Component: Form for property price prediction
• Property Recommendation Component: Interface for getting recommendations
• Navbar: Navigation and branding
• Hero Section: Landing page hero component
• Footer: Application footer

Backend Modules

• main.py: FastAPI application setup and route definitions
• recommendation.py: Property recommendation algorithm
• services/: Business logic implementation
• models/: Machine learning model management

Data

The system uses a property dataset stored in backend/datasets/appartments.csv containing various property features used for training and predictions.

Development

Code Style

• Frontend: ESLint + Prettier (TypeScript/React)
• Backend: PEP 8 compliance

Adding New Features

1. Frontend: Add components in components/ and wire them in app/page.tsx
2. Backend: Add routes in api/main.py and implement logic in service modules

Troubleshooting

Common Issues

1. Backend not connecting: Ensure the FastAPI server is running and NEXT_PUBLIC_API_URL is correctly configured
2. Port conflicts: Change ports in development commands if defaults are in use
3. Missing dependencies: Reinstall with pnpm install or pip install -r requirements.txt

Contributing

1. Create a feature branch
2. Make your changes
3. Test thoroughly
4. Submit a pull request

License

This project is part of the OpenMRS initiative.

Support

For issues or questions, please check the project documentation or contact the development team.