Publication

Abstract : Implementing machine learning and deep learning approaches for the solution identification for real-time problems like flight recommendation system based on the weather is a novel approach. This approach will be based on the weather prediction and it is a challenging approach, in which both current and forecasted weather data need to be available to provide an optimal result. Considering the weather parameters like temperature, wind speed, visibility and rainfall are the key parameters considered for the analysis and model implementation. This approach creates a safer approach of booking a safe journey based on the weather conditions and this can help the passengers to plan their journey accordingly. Deep learning weather forecast based recommendation model is based on advanced data simulating and analysing. ARIMA, LSTM, CNN and BILSTM are the considered model for this implementation, unlike other works that uses the old data to forecast. There is a requirement to gather the data from different sources in the form of API calls and this will create a pipeline to get the real-time data. These models are trained to predict the weather conditions allowing user to get a perfect recommendation to the most suitable and safe flight journey based on the weather conditions at the departure location, intermediate stops and final destination