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Predicting Hotel Reservation Cancellation by Using Machine Learning Methods

Year 2018, Volume: 1 Issue: 1, 7 - 14, 25.12.2018

Abstract

In order to maximize profit for hotels, occupancy
rates must be high. For this reason, hotels should allocate a limited number of
their rooms to the right customer at the right time using reservation systems
software. However, reservations may be cancelled by the customer for various
reasons. Cancellations may result for hotels in loss of income if the right
policies are

not processed. For this reason, it is very
important to estimate reservation cancellations.





In this study, the hotel reservation data set
consisting of 38,826 records of 5 different hotels were analyzed by machine
learning algorithms to estimate the cancellation of future bookings of hotels.
In this context, 4 different models were formed in this study by using Random
Forest (RF), Support Vector Machines (SVM), k-Nearest Neighbor (kNN) and
Decision Tree (C4.5) algorithms and then, performance comparisons were made
among these models. The best result was obtained from C4.5 decision tree
algorithm with 73% accuracy.

References

  • [1] Kimes SE, Wirtz J. “Has revenue management become acceptable? Findings from an international study on the perceived fairness of rate fences”. J. Serv. Res., 6(2), 125–135, 2003.
  • [2] Mehrotra R, Ruttley J. “Revenue management”. (2nd ed.). Washington, DC, American Hotel and Lodging Association, 2006.
  • [3] Smith SJ, Parsa HG, Bujisic M, van der Rest JP. “Hotel cancelation policies, distributive and procedural fairness, and consumer patronage: A study of the lodging industry”. J. Travel Tour. Mark., 32(7), 886–906, 2015.
  • [4] Morales DR, Wang J. “Forecasting cancellation rates for services booking revenue management using data mining”. Eur. J. Oper. Res., 202(2), 554–562, 2010.
  • [5] Liu PH. “Hotel demand/cancellation analysis and estimation of unconstrained demand using statistical methods”. Revenue Manag. Pricing Case Stud. Appl., 91–101, 2004.
  • [6] Carbonell CG, Michalski RS, Mitchell TM. “An overview of machine learning”. In Machine Learning, San Francisco, CA, Morgan Kaufmann, 3-23, 1983.
  • [7] Sullivan W. “Machine learning Beginners Guide Algorithms Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction”. USA, CreateSpace Independent Publishing Platform, 2017.
  • [8] Pölt S, “Forecasting is difficult–especially if it refers to the future”. In AGIFORS- Reservations and Yield Management Study Group Meeting Proceedings, 61–91, 1998.
  • [9] Antonio N, de Almeida A, Nunes L. “Predicting hotel booking cancellations to decrease uncertainty and increase revenue”, Tour. Manag. Stud., 13(2), 2017.
  • [10] Ho TK. “Random decision forests”. In Document analysis and recognition, 1995., proceedings of the third international conference on, 1, 278–282, 1995.
  • [11] Breiman L. “Random forests”. Mach. Learn., 45(1), 5–32, 2001.
  • [12] Louppe G. “Understanding Random Forests: From Theory to Practice”. Doktora Tezi, University of Liege, Belgium, 2014.
  • [13] Breiman L, Cutler A. “Random forests - classification description”. 2005. [Çevrimiçi]: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm. [Erişim: 10-Nis- 2018].
  • [14] Liaw A, Wiener M. “Classification and Regression by randomForest”. R News, 2(3), 18-22, 2002.
  • [15] Cunningham P, Delany SJ, “k-Nearest neighbour classifiers”, Mult. Classif. Syst., 34(8), 1–17, 2007.
  • [16] Balaban ME ve Kartal E, “Veri madenciliği ve makine öğrenmesi temel algoritmaları ve R dili ile uygulamaları”. 2. Baskı. İstanbul, Çağlayan Kitabevi, 2018.
  • [17] Bhatia N. “Survey of nearest neighbor techniques”, ArXiv Prepr. ArXiv10070085, 2010.
  • [18] Vapnik V, Lerner A. “Pattern recognition using generalized portrait method”. Autom. Remote Control, 24, 774-780, 1963.
  • [19] Nayak J, Naik B, Behera H. “A comprehensive survey on support vector machine in data mining tasks: applications & challenges”. Int. J. Database Theory Appl., 8(1), 169–186, 2015.
  • [20] Quinlan JR. “C4. 5: programs for machine learning”, CA, Morgan Kaufmann Publishers, 1993.
  • [21] Kartal E. “Sınıflandırmaya dayalı makine öğrenmesi teknikleri ve kardiyolojik risk değerlendirmesine ilişkin bir uygulama”. Doktora Tezi, İstanbul Üniversitesi, İstanbul, 2015.
  • [22] Kartal E, Özen Z. “Dengesiz Veri Setlerinde Sınıflandırma”. İçinde Mühendislikte Yapay Zekâ Uygulamaları, Sakarya, 109-131, 2017.
  • [23] Lunardon N, Menardi G, Torelli N, “ROSE: a Package for Binary Imbalanced Learning”.R J., 6(1), 82–92, 2014.
  • [24] R Core Team, “R: A Language and Environment for Statistical Computing”. 2017. [Çevrimiçi]: https://www.R-project.org/. [Erişim: 01-Eki-2018].
  • [25] Acuna E, The CASTLE Research Group “dprep: Data Pre-Processing and Visualization Functions for Classification”. 2015.
  • [26] Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F. “e1071: Misc Functions of the Department of Statistics”. Probability Theory Group (Formerly: E1071), TU Wien. 2017.
  • [27] Hornik K, Buchta C, Zeileis A. “Open-Source Machine Learning: R Meets Weka”,Comput. Stat., 24(2), 225–232, 2009.
  • [28] Hastie T, Tibshirani R, Friedman J. “The elements of statistical learning: Data Mining, Inference, and Prediction”. 1st ed., New York, Springer, 2001.
  • [29] Kuhn M., caret: Classification and Regression Training. 2018.

Otel Rezervasyon İptallerinin Makine Öğrenmesi Yöntemleri ile Tahmin Edilmesi

Year 2018, Volume: 1 Issue: 1, 7 - 14, 25.12.2018

Abstract

Konaklama
hizmeti veren otellerin maksimum kâr elde edebilmesi için doluluk oranlarının
yüksek olması gerekmektedir. Bu sebeple oteller rezervasyon sistemleri
aracılığıyla sınırlı sayıdaki odalarını doğru zamanda, doğru müşteriye tahsis
etmelidir. Ancak rezervasyonlar çeşitli nedenlerle müşteri tarafından iptal
edilebilmektedir. Oteller açısından iptal edilen rezervasyonlar doğru
politikalar izlenmezse gelir kaybına neden olabilmektedir. Bu sebeple
iptallerin önceden tahmin edilmesi büyük önem taşımaktadır.



Bu
çalışmada, makine öğrenmesi teknikleriyle 5 farklı otele ait toplam 38.826
kayıttan oluşan hotel rezervasyon verisi kullanılarak otellerin gelecekteki
rezervasyonlarının iptal durumları tahmin edilmeye çalışılmıştır. Çalışmada
sınıflandırma algoritmalarından Random Forest Algoritması (RF), Destek Vektör
Makineleri (SVM), k-En Yakın Komşu (kNN) ve Karar Ağacı (C4.5) algoritmaları
kullanılarak 4 farklı model oluşturulmuş ve modellerin performans
karşılaştırmaları yapılmıştır. En iyi sonuç %73 doğruluk oranı ile C4.5 karar
ağacı algoritmasından elde edilmiştir.

References

  • [1] Kimes SE, Wirtz J. “Has revenue management become acceptable? Findings from an international study on the perceived fairness of rate fences”. J. Serv. Res., 6(2), 125–135, 2003.
  • [2] Mehrotra R, Ruttley J. “Revenue management”. (2nd ed.). Washington, DC, American Hotel and Lodging Association, 2006.
  • [3] Smith SJ, Parsa HG, Bujisic M, van der Rest JP. “Hotel cancelation policies, distributive and procedural fairness, and consumer patronage: A study of the lodging industry”. J. Travel Tour. Mark., 32(7), 886–906, 2015.
  • [4] Morales DR, Wang J. “Forecasting cancellation rates for services booking revenue management using data mining”. Eur. J. Oper. Res., 202(2), 554–562, 2010.
  • [5] Liu PH. “Hotel demand/cancellation analysis and estimation of unconstrained demand using statistical methods”. Revenue Manag. Pricing Case Stud. Appl., 91–101, 2004.
  • [6] Carbonell CG, Michalski RS, Mitchell TM. “An overview of machine learning”. In Machine Learning, San Francisco, CA, Morgan Kaufmann, 3-23, 1983.
  • [7] Sullivan W. “Machine learning Beginners Guide Algorithms Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction”. USA, CreateSpace Independent Publishing Platform, 2017.
  • [8] Pölt S, “Forecasting is difficult–especially if it refers to the future”. In AGIFORS- Reservations and Yield Management Study Group Meeting Proceedings, 61–91, 1998.
  • [9] Antonio N, de Almeida A, Nunes L. “Predicting hotel booking cancellations to decrease uncertainty and increase revenue”, Tour. Manag. Stud., 13(2), 2017.
  • [10] Ho TK. “Random decision forests”. In Document analysis and recognition, 1995., proceedings of the third international conference on, 1, 278–282, 1995.
  • [11] Breiman L. “Random forests”. Mach. Learn., 45(1), 5–32, 2001.
  • [12] Louppe G. “Understanding Random Forests: From Theory to Practice”. Doktora Tezi, University of Liege, Belgium, 2014.
  • [13] Breiman L, Cutler A. “Random forests - classification description”. 2005. [Çevrimiçi]: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm. [Erişim: 10-Nis- 2018].
  • [14] Liaw A, Wiener M. “Classification and Regression by randomForest”. R News, 2(3), 18-22, 2002.
  • [15] Cunningham P, Delany SJ, “k-Nearest neighbour classifiers”, Mult. Classif. Syst., 34(8), 1–17, 2007.
  • [16] Balaban ME ve Kartal E, “Veri madenciliği ve makine öğrenmesi temel algoritmaları ve R dili ile uygulamaları”. 2. Baskı. İstanbul, Çağlayan Kitabevi, 2018.
  • [17] Bhatia N. “Survey of nearest neighbor techniques”, ArXiv Prepr. ArXiv10070085, 2010.
  • [18] Vapnik V, Lerner A. “Pattern recognition using generalized portrait method”. Autom. Remote Control, 24, 774-780, 1963.
  • [19] Nayak J, Naik B, Behera H. “A comprehensive survey on support vector machine in data mining tasks: applications & challenges”. Int. J. Database Theory Appl., 8(1), 169–186, 2015.
  • [20] Quinlan JR. “C4. 5: programs for machine learning”, CA, Morgan Kaufmann Publishers, 1993.
  • [21] Kartal E. “Sınıflandırmaya dayalı makine öğrenmesi teknikleri ve kardiyolojik risk değerlendirmesine ilişkin bir uygulama”. Doktora Tezi, İstanbul Üniversitesi, İstanbul, 2015.
  • [22] Kartal E, Özen Z. “Dengesiz Veri Setlerinde Sınıflandırma”. İçinde Mühendislikte Yapay Zekâ Uygulamaları, Sakarya, 109-131, 2017.
  • [23] Lunardon N, Menardi G, Torelli N, “ROSE: a Package for Binary Imbalanced Learning”.R J., 6(1), 82–92, 2014.
  • [24] R Core Team, “R: A Language and Environment for Statistical Computing”. 2017. [Çevrimiçi]: https://www.R-project.org/. [Erişim: 01-Eki-2018].
  • [25] Acuna E, The CASTLE Research Group “dprep: Data Pre-Processing and Visualization Functions for Classification”. 2015.
  • [26] Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F. “e1071: Misc Functions of the Department of Statistics”. Probability Theory Group (Formerly: E1071), TU Wien. 2017.
  • [27] Hornik K, Buchta C, Zeileis A. “Open-Source Machine Learning: R Meets Weka”,Comput. Stat., 24(2), 225–232, 2009.
  • [28] Hastie T, Tibshirani R, Friedman J. “The elements of statistical learning: Data Mining, Inference, and Prediction”. 1st ed., New York, Springer, 2001.
  • [29] Kuhn M., caret: Classification and Regression Training. 2018.
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mehmet Boz This is me

Erokan Canbazoğlu

Zeki Özen

Sevinç Gülseçen

Publication Date December 25, 2018
Published in Issue Year 2018 Volume: 1 Issue: 1

Cite

APA Boz, M., Canbazoğlu, E., Özen, Z., Gülseçen, S. (2018). Otel Rezervasyon İptallerinin Makine Öğrenmesi Yöntemleri ile Tahmin Edilmesi. Veri Bilimi, 1(1), 7-14.



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