Flight Delay Prediction using Advanced Machine Learning
The airline faced significant losses estimated at US$ 40MM annually due to flight delays, generating additional operational costs, passenger compensation and reputational damage. Traditional prediction methods showed poor performance (F1-score < 0.55), and did not adequately capture route-specific, time-specific or seasonal delay patterns. They also lacked capacity to identify anomalies or cluster delay types to implement differentiated strategies. The goal was to reduce delay-associated costs by at least 5% through accurate predictions that would allow preventive measures to be taken with sufficient advance notice.
Description
Predictive Modeling for Flight Delays
We developed an advanced flight delay prediction system using machine learning techniques for an international airline. The system integrates classifier models (LightGBM, Random Forest, Logistic Regression), clustering analysis to identify distinctive patterns, and anomaly detection to manage exceptional events. Through advanced optimization, we achieved an F1-score of 0.722, effectively identifying flights with high probability of significant delays (>15 minutes). The generated recommendations enabled implementation of differentiated strategies by delay type, generating estimated annual savings of US$ 25-40MM through proactive operations and resource management.
Technologies
Objectives
The airline faced significant losses estimated at US$ 40MM annually due to flight delays, generating additional operational costs, passenger compensation and reputational damage. Traditional prediction methods showed poor performance (F1-score < 0.55), and did not adequately capture route-specific, time-specific or seasonal delay patterns. They also lacked capacity to identify anomalies or cluster delay types to implement differentiated strategies. The goal was to reduce delay-associated costs by at least 5% through accurate predictions that would allow preventive measures to be taken with sufficient advance notice.
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