A Flight Fare Prediction Using Machine Learning

International Journal of Scientific & Technology Research, 2019

Someone who purchase flight tickets frequently would be able to predict the right time to procure a ticket to obtain the best deal. Many airlines change ticket prices for their revenue management. The airline may increase the prices when the demand is to be expected to increase the capacity. To estimate the minimum airfare, data for a specific air route has been collected including the features like departure time, arrival time and airways over a specific period. Features are extracted from the collected data to apply Machine Learning (ML) models. This paper gives the machine learning regression methods to predict the prices at the given time. Index Terms : Machine Learning Algorithm, Predictor, airfare, bagging regression.. ————————————————————

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Many people nowadays choose to travel by flights. The cost of an airline ticket has a significant impact on a traveller's decision on which mode of transportation to use. A wide number of factors influence the price of an airline ticket, including social, competitive, marketing, and financial factors, among others. Every airline has a different technique for determining ticket prices. We can uncover the rules that airlines may use to model their fare variation using Machine Learning. In this project paper, we propose developing a web-based application for projecting the price of a flight ticket using Kaggle data, where the dataset contains various data related to 10,000 flights. The framework proposed will be used to simulate several regression algorithms for estimating projected flight fares. The model that will produce extremely accurate forecasts will be finalized, and it will solely be utilised to forecast the price.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

With ever increasing air route connectivity throughout the world, air travel has become a common, integral and faster way to travel. Predicting fares for airlines is an important as well as challenging task since a constant fluctuation in fares is observed and it is known to be dependent on varied set factors. With tremendous study in area, it is observed that using Machine Learning, Artificial Intelligence and Deep Learning techniques an estimation of flight fares at a given time can be obtained within seconds. In this paper, we use a Machine Learning Regression approach to predict flight fare by providing basic details of departure date and time, arrival time, source, destination, number of stops and name of the airline. The results show that Random Forest Regression Model provides highly optimal results.

International Journal of Advanced Trends in Computer Science and Engineering, 2021

As domestic air travel in India is becoming increasingly popular with different air ticket booking channels coming online these days, passengers are trying to understand how these airline companies make decisions over time about ticket prices. Therefore, many methods are ready to provide the proper time to do so. The customer who buys an air ticket by estimating the price of the airfare is recently proposed. The majority of these strategies make use of sophisticated Computational Intelligence Prediction Models an area of science known as Machine Learning (ML). This paper highlights the parameters and also includes the guidelines that are important for project work to be developed that is indicated above.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

The Flight ticket prices increase or decrease every now and then depending on various factors like timing of the flights, destination, duration of flights. In the proposed system a predictive model will be created by applying machine learning algorithms to the collected historical data of flights. Optimal timing for airline ticket purchasing from the consumer's perspective is challenging principally because buyers have insufficient information for reasoning about future price movements. In this project we majorly targeted to uncover underlying trends of flight prices in India using historical data and also to suggest the best time to buy a flight ticket. The project implements the validations or contradictions towards myths regarding the airline industry, a comparison study among various models in predicting the optimal time to buy the flight ticket and the amount that can be saved if done so. Remarkably, the trends of the prices are highly sensitive to the route, month of departure, day of departure, time of departure, whether the day of departure is a holiday and airline carrier. Highly competitive routes like most business routes (tier 1 to tier 1 cities like Mumbai-Delhi) had a non-decreasing trend where prices increased as days to departure decreased, however other routes (tier 1 to tier 2 cities like Delhi-Guwahati) had a specific time frame where the prices are minimum. Moreover, the data also uncovered two basic categories of airline carriers operating in India-the economical group and the luxurious group, and in most cases, the minimum priced flight was a member of the economical group. The data also validated the fact that, there are certain time-periods of the day where the prices are expected to be maximum. The scope of the project can be extensively extended across the various routes to make significant savings on the purchase of flight prices across the Indian Domestic Airline market.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

: In the airline industry, ticket pricing is a complex process that is influenced by various factors, including demand, availability, and competition. Pricing strategies that enable airlines to maximize profits while maintaining customer satisfaction are therefore essential. By analysing historical data, machine learning algorithms can identify a minimum airfare, data for a specific air route has been collected including the features like arrival time, departure time and airways over a specific time period, features are extracted from the collected data to apply Machine Learning (ML) models.

2017 25th European Signal Processing Conference (EUSIPCO), 2017

This paper deals with the problem of airfare prices prediction. For this purpose a set of features characterizing a typical flight is decided, supposing that these features affect the price of an air ticket. The features are applied to eight state of the art machine learning (ML) models, used to predict the air tickets prices, and the performance of the models is compared to each other. Along with the prediction accuracy of each model, this paper studies the dependency of the accuracy on the feature set used to represent an airfare. For the experiments a novel dataset consisting of 1814 data flights of the Aegean Airlines for a specific international destination (from Thessaloniki to Stuttgart) is constructed and used to train each ML model. The derived experimental results reveal that the ML models are able to handle this regression problem with almost 88% accuracy, for a certain type of flight features.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

There are several differences in today's continuously fluctuating airline ticket prices. Within a few hours, the same flight's price continues changing. The shoppers want to get the best deal possible, while the airline corporations want to make as much money as they can. Researchers have developed many models to address this issue, including ones that estimate the minimal price and the best time to buy a ticket, while airlines use strategies like demand forecasting and price discrimination to increase their profits.

Optimal timing for airline ticket purchasing from the consumer's perspective is challenging principally because buyers have insufficient information for reasoning about future price movements. This paper presents a model for computing expected future prices and reasoning about the risk of price changes. The proposed model is used to predict the future expected minimum price of all available flights on specific routes and dates based on a corpus of historical price quotes. Also, we apply our model to predict prices of flights with specific desirable properties such as flights from a specific airline, non-stop only flights, or multi-segment flights. By comparing models with different target properties, buyers can determine the likely cost of their preferences. We present the expected costs of various preferences for two high-volume routes. Performance of the prediction models presented is achieved by including instances of time-delayed features, by imposing a class hierarchy among the raw features based on feature similarity, and by pruning the classes of features used in prediction based on in-situ performance. Our results show that purchase policy guidance using these models can lower the average cost of purchases in the 2 month period prior to a desired departure. The proposed method compares favorably with a deployed commercial web site providing similar purchase policy recommendations.

Optimal timing for airline ticket purchasing from the consumer's perspective is challenging principally because buyers have insufficient information for reasoning about future price movements. This paper presents a model for computing expected future prices and reasoning about the risk of price changes. The proposed model is used to predict the future expected minimum price of all available flights on specific routes and dates based on a corpus of historical price quotes. Also, we apply our model to predict prices of flights with specific desirable properties such as flights from a specific airline, non-stop only flights, or multi-segment flights. By comparing models with different target properties, buyers can determine the likely cost of their preferences. We present the expected costs of various preferences for two high-volume routes. Performance of the prediction models presented is achieved by including instances of time-delayed features, by imposing a class hierarchy among the raw features based on feature similarity, and by pruning the classes of features used in prediction based on in-situ performance. Our results show that purchase policy guidance using these models can lower the average cost of purchases in the 2 month period prior to a desired departure. The proposed method compares favorably with a deployed commercial web site providing similar purchase policy recommendations.