Airline Seat Reservation System in PHP

The allocation of class seats on a flight in airline industry is closely connected to multiple correlating factors, including yield management and airfare strategy from airlines, policy regulation and price modification from travel agencies, booking and reservation behavior from customers. Different from various machine learning methods targeting at direct fare or price prediction, we constructed a state predictor of class seats by applying a Naïve Bayes algorithm based on Multinomial Event Model on the core flight reservations inventory big data, to tell the probability of class availability within the next several hours or days. Four fundamental models and one integrated model are developed to propose an optimal decision to the airfare search engine layer, which makes the engine be capable of forecasting a smart buy-or-wait suggestion to customers. In our experimental route from SHA to TYO, the integrated model reaches an average of 95.42% accuracy.