Improving Customer Retention in Nigeria’s Aviation Industry: A Machine Learning Perspective

Nigeria’s aviation sector faces intense competition, rising operational costs, and volatile passenger loyalty. This study employs a Random Forest classifier to predict passenger churn using anonymized flight data, developing a model that achieves high precision in identifying at-risk passengers. Key predictors include delayed flight duration, customer service interactions, and travel class. The results inform targeted retention strategies, such as predictive dashboards and loyalty programs, offering actionable insights for airline operations and revenue protection.

Machine Learning; Customer Retention

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