Flight Price Prediction System

Description

This project uses machine learning to predict flight prices based on booking dates, departure dates, airlines, and routes. It helps travellers make informed booking decisions by forecasting future flight prices and providing a practical solution to the problem of fluctuating airfare costs.

Problem Statement

Flight prices fluctuate significantly due to booking time, seasonality, airline, and demand. This unpredictability makes it challenging for travellers to find the best prices, often leading to overpayment or missed opportunities for better deals.

Solution

Developing a flight price prediction system using machine learning to forecast future prices based on historical data and relevant features. This system will help travellers identify the best times to book flights, saving money and reducing booking uncertainty.

Features

• Load and preprocess flight price data.
• Train machine learning models (Random Forest) for price prediction.
• Visualize prediction results with plots.

Libraries Used

• Pandas
• NumPy
• Scikit-learn
• Matplotlib

Installation

# Clone the repository
git clone https://github.com/Ableboy/flight-price-prediction.git

# Navigate into the project directory
cd flight-price-prediction

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Use Case

This project is designed to help travellers make informed decisions about when to book flights by predicting future prices. It is also useful for educational purposes in learning and demonstrating machine learning regression algorithms.

Contributing

Contributors are welcome to this project! To contribute, please follow these steps:

1. Fork the repository on GitHub.
2. Clone your forked repository to your local machine.

   git clone https://github.com/Ableboy/flight-price-prediction.git

3. Create a branch for your feature or bug fix.

   git checkout -b feature/feature-name

4. Commit your changes with a clear message.

   git commit -m 'Add some feature'

5. Push your changes to your forked repository.

   git push origin feature/feature-name

6. Open a pull request against the main repository.

Please ensure your code adheres to the existing coding conventions and includes tests for any new functionality.

License

MIT License