Skykash: Random Forest-Based Airfare Price Prediction System
Keywords:
Airfare price prediction, Gradient boosting, Machine learning, Random forest, Regression analysis, Web-Based decision support systemAbstract
Airline ticket prices fluctuate dynamically due to multiple factors such as booking time, route demand, seasonal variations, and airline pricing strategies. This variability makes it challenging for travelers to determine the optimal time to purchase tickets. This study proposes Skykash, a web-based airfare price prediction system that leverages supervised machine learning techniques to forecast ticket prices accurately. The system evaluates three predictive models: Linear Regression, Random Forest, and Gradient Boosting using a dataset of 100,000 Indian flight booking records collected between 2023 and 2025. Model performance is assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results indicate that Gradient Boosting outperforms other models with the lowest RMSE (705) and MAE (510), demonstrating its effectiveness in capturing nonlinear pricing behavior. The predictive engine is integrated with a Flask backend and React-based user interface to provide interactive visualization and decision support. The proposed system assists travelers in identifying cost-effective booking periods and supports airline stakeholders in implementing data-driven pricing strategies.
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