Flight-Fare-Prediction | End to end implementation and deployment of Machine Learning Airline Flight Fare Prediction using p

Flight-Fare-Prediction is a Jupyter Notebook library. Flight-Fare-Prediction has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

The Airline Flight Fare Prediction is a Flask web application to predict airline flight fares across the Indian cities. The dataset for the project is taken from Kaggle, and it is a time-stamped dataset so, while building the model, extensive pre-processing was done on the dataset especially on the date-time columns to finally come up with a ML model which could effectively predict airline fares across various Indian Cities. The dataset had many features which had to pre-processed and transformed into new parameters for a cleaner and simple web application layout to predict the fares. The various independent features in the dataset were:. Airline: The name of the airline. Date_of_Journey: The date of the journey. Source: The source from which the service begins. Destination: The destination where the service ends. Route: The route taken by the flight to reach the destination. Dep_Time: The time when the journey starts from the source. Arrival_Time: Time of arrival at the destination. Duration: Total duration of the flight. Total_Stops: Total stops between the source and destination. Additional_Info: Additional information about the flight. Price: The price of the ticket.