Flight Ticket Price Prediction Using Decision Tree Classifier
Keywords:
Decision Tree, Decision Tree Classifier, Flight Price Prediction, Feature Importance, Regression, ClassificationAbstract
In today’s fast-paced and highly competitive airline industry, accurately predicting flight prices plays a vital role for both travelers looking for budget-friendly fares and airlines aiming to maximize their revenue. This study explores the application of Decision Tree and Decision Tree Classifier algorithms to assess their effectiveness in flight price prediction. The Decision Tree model is examined for its simplicity and its strength in capturing non-linear relationships, making it useful for identifying pricing patterns and influential variables in airfare data. At the same time, the Decision Tree Classifier is used to group flight prices into specific categories, offering a classification-based perspective on price behavior. Using real-world flight pricing data, the models are evaluated based on performance metrics including R² score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Feature importance analysis is also conducted to identify the key factors influencing fare changes. By focusing on these two tree-based algorithms, the study highlights their potential in both regression and classification tasks within the context of flight price prediction. The insights gained can support smarter decision-making for both consumers and airline operators, leading to more efficient pricing strategies in the aviation sector.
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