TrueRoute

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Inspiration

Flight delays and cancellations cause stress, missed connections, and wasted time. In fact, our own team faced delays while traveling to this hackathon, reinforcing the need for a smarter way to choose reliable flights. We wanted to build a solution that helps travelers avoid disruptions by using data-driven predictions, ensuring a smoother travel experience.

What it does

TrueRoute analyzes historical flight data, weather patterns, and passenger sentiment to predict reliability. It assigns a score to flights based on delay risks, cancellation likelihood, and user experience, helping travelers make informed choices for smoother journeys.

How we built it

We leveraged machine learning models trained on historical flight performance, weather forecasts, and sentiment analysis from passenger reviews. The backend processes real-time and historical data to generate reliability scores, while the frontend delivers a seamless, user-friendly search experience.

Challenges we ran into

Aggregating and cleaning large datasets from multiple sources
Building accurate predictive models with constantly changing variables
Ensuring real-time responsiveness for a smooth user experience
Integrating various APIs efficiently while maintaining performance

Accomplishments that we're proud of

Successfully predicting flight reliability with a high degree of accuracy
Developing a scoring system that balances data, weather conditions, and user sentiment
Creating an intuitive interface that makes flight selection effortless
Overcoming technical challenges to process large datasets efficiently

What we learned

The complexity of flight disruptions and how various factors interconnect
Effective ways to process and analyze vast amounts of historical and real-time data
How to optimize machine learning models for dynamic predictions
The importance of a seamless UX/UI for travel-based applications