Abstract
Online hotel booking became increasingly popular as time passed, and with its popularity, the data that can be collected based on customer actions has increased. This data can serve to build intelligent systems that can provide knowledge for both customers and hotel owners. In this paper, we focus on hotel owners who can benefit from the collected data by adjusting the prices to optimise the profit of their accommodations. To accomplish this, we built a system that collected the data from Booking.com and gathered a helpful dataset for price prediction. We used five regression algorithms and an optimization technique to obtain the best results, leading us to a 9% error for price prediction. This result allows accommodation owners to predict the room price to keep the rooms fully occupied.
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References
Al Shehhi, M., Karathanasopoulos, A.: Forecasting hotel room prices in selected GCC cities using deep learning. J. Hosp. Tour. Manag. 42, 40–50 (2020)
Awad, M., Khanna, R., Awad, M., Khanna, R.: Support vector regression. In: Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, pp. 67–80 (2015)
Dhillon, J., et al.: Analysis of Airbnb prices using machine learning techniques. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0297–0303. IEEE (2021)
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., Hutter, F.: Auto-sklearn 2.0: the next generation. arXiv preprint arXiv:2007.04074, vol. 24 (2020)
Hu, T., Song, H.: Analysis of influencing factors and distribution simulation of budget hotel room pricing based on big data and machine learning from a spatial perspective. Sustainability 15(1), 617 (2022)
Jhalani, T., Kant, V., Dwivedi, P.: A linear regression approach to multi-criteria recommender system. In: Tan, Y., Shi, Y. (eds.) DMBD 2016. LNCS, vol. 9714, pp. 235–243. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40973-3_23
Liu, Y., Wang, Y., Zhang, J.: New machine learning algorithm: random forest. In: Liu, B., Ma, M., Chang, J. (eds.) ICICA 2012. LNCS, vol. 7473, pp. 246–252. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34062-8_32
Liu, Y.: Airbnb pricing based on statistical machine learning models. In: 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML), pp. 175–185. IEEE (2021)
Mitchell, R., Adinets, A., Rao, T., Frank, E.: XGBoost: scalable GPU accelerated learning. arXiv preprint arXiv:1806.11248 (2018)
Rezazadeh Kalehbasti, P., Nikolenko, L., Rezaei, H.: Airbnb price prediction using machine learning and sentiment analysis. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2021. LNCS, vol. 12844, pp. 173–184. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84060-0_11
Saunders, C., Gammerman, A., Vovk, V.: Ridge regression learning algorithm in dual variables (1998)
Seber, G.A., Lee, A.J.: Linear Regression Analysis. Wiley, Hoboken (2012)
Shirisha, N., Anusha, K., Kiran, A., Buavani, Y.T.S.: Prediction of hotel booking & cancellation using machine learning algorithms. In: 2023 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–4. IEEE (2023)
Sinaga, K.P., Yang, M.S.: Unsupervised k-means clustering algorithm. IEEE Access 8, 80716–80727 (2020)
Song, Y., Liang, J., Lu, J., Zhao, X.: An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing 251, 26–34 (2017)
Testas, A.: Decision tree regression with pandas, scikit-learn, and PySpark. In: Testas, A. (ed.) Distributed Machine Learning with PySpark: Migrating Effortlessly from Pandas and Scikit-Learn, pp. 75–113. Springer, Cham (2023). https://doi.org/10.1007/978-1-4842-9751-3_4
Viverit, L., Heo, C.Y., Pereira, L.N., Tiana, G.: Application of machine learning to cluster hotel booking curves for hotel demand forecasting. Int. J. Hosp. Manag. 111, 103455 (2023)
Yang, S.: Learning-based Airbnb price prediction model. In: 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT), pp. 283–288. IEEE (2021)
Zhu, F., et al.: Modeling price elasticity for occupancy prediction in hotel dynamic pricing. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 4742–4746 (2022)
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Bălan, A., Popescu, P.Ş., Mihăescu, M.C. (2025). Hotel’s Price Prediction Based on Country Specific Data. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_3
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