The passengers are not allowed to access the previous flight price data to predict the best price for them, but the airlines have all the information about that. In this research, we tried to find a best model for predicting airfare that can help the passenger get the best predicted travel price. After all, we have the best model: Random Forest Regression Algorithm to predict the price of airline tickets.
For that reason, many researchers are looking for a best model through which the passengers can get the best airfare price to travel and save money. For that reason, we try to find the best model to predict the best price. We use these algorithms to find the best model to predict the airline tickets among them.
We try to do something by which the customers can know the lowest and best prices for air tickets and the best time to buy the tickets from the airlines. Predicting the price of airline tickets can be so helpful for travel agents and customers to decide the best time to buy or purchase tickets from the airlines. From the results of these algorithms, we can find out the best prediction model to predict the flight prices with a better accuracy.
Airline ticket price forecasting so customers can be sure of the best time to buy tickets.
BACKGROUND 4-6
Related Work 4-6
Experiment Data Set 07
After that we imported our dataset to run and got the train data format (10000,11).
Data Pre-Processing 8-12
Airline, Date of travel, Source, Destination, Route, Departure time, Arrival time, Duration, Total number of stops, Additional info and price segment in our dataset. Then we found a number of unique destinations like New Delhi, Bangalore, Cochin, Delhi and Hyderabad. We selected some features to extract such as travel date, arrival time, departure time.
We have extracted the minimum, maximum values of the travel date and extracted it into the travel date, travel month. After extracting the Date_of_Journey column we have extracted Arrival_time into Arrival_hour and Arrival_min. We then took the additional information from the preprocessed data, counted it and divided it by the length of the data by 100, and converted it to a numpy array.
We then removed the route, additional information, total minutes of duration, and year of travel from the preprocessed dataset. The model is designed to predict the airline tickets for the passengers, thereby saving the passengers money. After collecting the dataset, we performed data preprocessing, including data cleaning, and then preprocessed the data.
We used three algorithms such as Random Forest Regression, Decision Tree and Linear Regression to find the best model of them. It is a technique that combines predictions from multiple decision trees to make a more accurate prediction than a single model. During training, this algorithm works by creating several decision trees and collecting the results of all the decision trees and using the average value to predict the final result [10][11].
For a new data point, have each of the N trees predict the value of y for that data point and assign the new point to the average across all the predicted y values. The Decision Tree Algorithm is a supervised learning algorithm that can be used for both classification and regression problems. Step-5: Recursively create new decision trees using the subsets of the data set created in step-3.
A linear regression algorithm performs the task of predicting the value of a dependent variable (y) based on a given independent variable (x).
Training and testing 18
Implementation of our Model 18
Finally the Machine Learning models are ready and x_train and y_train are fitted into these models. After successful training, x-test set can be used to predict y-test set, where y-test set is the actual price of the airline ticket. The results for the decision tree algorithm are 79.20% and for the linear regression algorithm it is 72.77%, which is not that bad.
In this research we have developed a system that can predict the flight price for consumers. There are many machine learning algorithms that have been used in previous research to predict the price of airline tickets, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN) etc. We applied three ML algorithms such as Random Forest Regression Algorithm , Decision Tree Algorithm and Linear Regression Algorithm to compare their results and find the best model to predict the airline tickets for the passengers.
We found that random forest gives better accuracy than other algorithms, which is more important. Price prediction takes a short time, which is also beneficial for the customer. This will make our system more complete, the performance of the prediction model will be better, and the accuracy will be more effective.
34;A Framework for Airfare Price Prediction: A Machine Learning Approach IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), 2019. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 119– 128. Diamantaras, “Flight Price Prediction Using Machine Learning Techniques,” in 25th IEEE European Signal Processing Conference, 2017, p.
Barbon Jr, "Deep Regressor Regularization for Airline Ticket Price Forecasting," in XIII Brazilian Symposium on Information Systems: Information Systems for Participatory Digital Governance. Available at
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