Flight Fare Prediction App with Deployment Machine Learning

Flight Fare Prediction App with Deployment Machine Learning

Authors -Assistant Professor Mr.C.Bastin Rogers, Sujin .T, Aron Herso.S, Sabin .M

Abstract- – The development of a flight fare prediction web application with deployment and machine learning encompasses a comprehensive process. This involves acquiring historical flight data, performing data preprocessing and feature engineering, selecting an appropriate regression algorithm, and training the model using split data sets. The implementation includes develop- ing a web application through frameworks like Flask or Django, creating an intuitive user interface for inputting flight details, and deploying the trained model as an API or web service on cloud platforms such as AWS or GCP. The integration of the web application with the model API enables predictions based on user inputs. The process concludes with thorough testing and debugging to ensure functionality, deployment to a production environment, and ongoing monitoring, maintenance, and updates as required.

DOI: /10.61463/ijset.vol.11.issue2.393