A Framework for Predicting Airfare Prices Using Machine Learning | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 8, Volume 22, Issue 3, September 2022, Pages 81-96 PDF (1.13 M) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.22.3.8 | ||
| Authors | ||
| Heba Mohammed Fadhil* 1; Mohammed Najm Abdullah2; Mohammed Issam Younis3 | ||
| 1baghdad/al-adhmyia | ||
| 2Computer Engineering Dept, University of Technology, Baghdad, Iraq | ||
| 3Department of Computer Engineering, College of Engineering, University of Baghdad Al-Jadriyah, Baghdad, Iraq | ||
| Abstract | ||
| Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K- nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion. | ||
| Keywords | ||
| Machine learning; Prediction model; Airline price prediction; Software testing | ||
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