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Welcome to the FlightDelay Wiki!

Project Overview

FlightDelay is a real-time flight delay prediction system that uses streaming data processing and machine learning to predict severe flight delays before they happen. The system ingests live flight data through Apache Kafka, processes it with Apache Spark, and applies trained Gradient Boosted Tree models to predict whether flights will experience delays of 60+ minutes. Users can view real-time predictions through an interactive web dashboard and explore comprehensive visualizations of historical flight data patterns.

Target Users

• Airline Operations Teams – Proactively manage resources and passenger notifications based on delay predictions
• Airport Staff – Optimize gate assignments, crew scheduling, and ground operations
• Travel Agencies – Provide clients with early warnings and alternative flight options
• Passengers – Make informed decisions about travel plans and connections
• Data Scientists – Study flight delay patterns and model performance across different scenarios
• Aviation Analysts – Understand delay trends by airline, airport, time, and other factors
• Researchers – Explore large-scale aviation data and streaming ML applications

Motivation

FlightDelay addresses the challenge of flight delays by providing predictive insights before delays occur, enabling stakeholders to take proactive action. Traditional delay information is reactive—passengers and airlines only learn about delays after they've already happened. By combining real-time data streaming with machine learning, FlightDelay analyzes patterns in scheduled departure times, airlines, airports, routes, distances, and temporal features to predict severe delays with meaningful accuracy. The system demonstrates how big data technologies (Kafka, Spark) can be integrated with ML pipelines to create actionable, real-time intelligence from streaming data.

Result

A scalable, end-to-end streaming analytics platform that predicts flight delays in real-time and provides actionable insights. The system processes thousands of flight records per second, applies sophisticated machine learning models trained on historical patterns, and delivers predictions through both a live web dashboard and exportable data formats. With comprehensive visualizations spanning delay causes, airport performance, airline reliability, and temporal trends, FlightDelay demonstrates the power of streaming machine learning for operational decision-making in the aviation industry.