Air transport provides efficient, well organized, and time effective services. Even though flights are the fastest way to transport, its delay leads to customer dissatisfaction. Many factors effect flight delays, some of them are weather, operational imperfection, baggage loading etc. In this paper, we are developing a predictive system which predicts flight delays based on weather data. Flightdata set taken from US DEPARTMENT OF TRANSPORTATION and weather data set from HOURLY LAND-BASED WEATHER OBSERVATIONS FROM NOAA. We have implemented Ensemble method, Decision tree and Random forest on the balanced data set. For balancing the data set we are using sampling techniques. The algorithms are applied on the combined flight and weather data set to predict the flight delays.

Flights, Weather, Random Forest, Decision Tree, Ensemble

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