• Shabana Tadvi
• Mariya Khan
• Afifa Shaikh
• Naziya Begum Sayyed

Ticket purchasing in advance is a well- known traditional approach but it entirely depends on the Airline industry to change the fare according to factors whether the travel is during the holidays, the number of free seats in the plane etc. Some of the features are seen, but some of them remained hidden. We are using Indian Domestic Airline Dataset which contains multiple columns so over a period as the data increases (approx. 1 year) we will be able to extract few more hidden features to increase the efficiency and accuracy of the system. The goal is to use machine learning techniques to model the behavior of flight ticket prices over the time. In other words system will be able to provide a general idea to the clients when to increase or decrease the fares i.e. prediction of Airfare. For that after collecting the dataset the proposed system will extract important features from dataset, cleaning of data and using Regression Machine Learning Algorithms multiple models will be trained and the accuracy of those models will be compared and prediction report will be given to client.

Airfare, Regression, Data Analytics, Airline, Machine Learning , Prediction.

"A Novel Data Science And ML Approach To Predict Airfare", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.322-327, April-2019, Available :http://www.jetir.org/papers/JETIR1904660.pdf

"A Novel Data Science And ML Approach To Predict Airfare", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp322-327, April-2019, Available at : http://www.jetir.org/papers/JETIR1904660.pdf