ML-Model link: https://www.kaggle.com/code/saiswaroop8656/team-byteforge

The app can be visited at https://nutanixps.vercel.app/

Flight Price Prediction System

A full-stack application for predicting flight prices using machine learning models and visualizing price trends through interactive graphs.

Project Overview

This project consists of two main components:

• A modern React frontend for user interaction and data visualization
• A Python FastAPI backend powered by machine learning for price predictions

Features

Frontend

• Modern, responsive UI built with React and TailwindCSS
• Interactive flight search form
• Real-time price predictions
• Dynamic graph visualizations using Chart.js
• Smooth animations and transitions
• Mobile-friendly design

Backend

• FastAPI-based REST API
• Machine learning models for price prediction
• Data processing and analysis
• CSV data integration
• Efficient API endpoints

Tech Stack

Frontend

• React 19
• Vite
• TailwindCSS
• Chart.js
• React Router DOM
• Axios
• ESLint

Backend

• Python
• FastAPI
• Scikit-learn
• XGBoost
• Pandas
• NumPy
• Uvicorn

Prerequisites

Frontend

• Node.js (v14.0.0 or higher)
• npm (v6.0.0 or higher)

Backend

• Python 3.8 or higher
• pip (Python package manager)

Installation

1. Clone the repository:

git clone <repository-url>
cd flight-price-prediction

2. Frontend Setup:

cd nutanix-frontend
npm install

3. Backend Setup:

cd nutanix-backend
pip install -r requirements.txt

Running the Application

1. Start the Backend Server:

cd nutanix-backend
uvicorn server:app --reload

2. Start the Frontend Development Server:

cd nutanix-frontend
npm run dev

The frontend will be available at http://localhost:5173 and the backend API at http://localhost:8000.

Project Structure

flight-price-prediction/
├── nutanix-frontend/
│   ├── src/
│   ├── public/
│   ├── package.json
│   └── vite.config.js
├── nutanix-backend/
│   ├── app/
│   ├── flights.csv
│   ├── available.csv
│   ├── requirements.txt
│   └── run_servers.py
└── README.md

API Endpoints

The backend provides several endpoints for:

• Flight price predictions
• Historical price data
• Available flight routes
• Price trend analysis

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.