Perakende Sektöründe Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Performans Analizi: Black Friday Satış Tahminlemesi

Yıl 2024, , 65 - 90, 30.04.2024

Vahid Sinap

https://doi.org/10.29249/selcuksbmyd.1401822

Öz

Büyük perakende zincirlerinin şube ağlarının genişlemesi, müşteri tabanlarının büyümesi ve artan müşteri profili heterojenliği satış tahminleme süreçlerinin karmaşıklığını artırmaktadır. Müşteri çeşitliliği ve bu çeşitliliğin yönetilmesi, perakendeciler için hem stratejik planlama hem de operasyonel düzeyde uygulama açısından önemli bir güçlük oluşturmaktadır. Bu noktada, müşteri segmentasyonu ve kişiselleştirilmiş pazarlama stratejileri geliştirmek, her bir müşteri grubuna özel yaklaşımlar belirlemek ve bu çeşitliliği anlayarak etkili bir şekilde yönetmek önem kazanmaktadır. Gelişen teknolojiler, özellikle makine öğrenmesi yöntemleri söz konusu zorluklarla başa çıkma potansiyeli sunmaktadır. Bu kapsamda araştırmanın amacı, bir perakende firmasının Black Friday günündeki satış veri seti üzerinde Doğrusal Regresyon, Rastgele Orman Regresyonu, K-En Yakın Komşu Regresyonu, XGBoost Regresyonu, Karar Ağacı Regresyonu ve LGBM Regresyonu isimli makine öğrenmesi algoritmaları aracılığıyla satış tahminlemesi gerçekleştirmek ve algoritmaların performanslarını karşılaştırarak en iyi performans gösteren algoritmayı belirlemektir. Ayrıca, GridSearchCV kullanarak hiperparametrelerin ayarlanması ve bu ayarlamaların modellerin performanslarına etkisinin incelenmesi amaçlanmaktadır. Buna ek olarak, veri seti üzerinde Keşifsel Veri Analizleri yürütülerek, perakende sektöründeki işletmelerin ellerinde bulunan verilerden ne tür bilgiler çıkarabileceklerine ve bu bilgileri nasıl değerlendirebileceklerine ilişkin bir örnek oluşturmak araştırmanın diğer önemli bir amacıdır. Araştırmadan elde edilen sonuçlara göre, satışları tahminlemede en başarılı algoritma GridSearchCV ile hiperparametreleri ayarlanmış XGBoost Regresyonu olmuştur. Firma müşterilerinin en çok 26-35 yaş aralığında bireylerden oluştuğu, erkek müşterilerin kadınlara, bekar müşterilerin evlilere göre önemli ölçüde daha yüksek tutarlı alışverişler yaptığı saptanmıştır. Ayrıca, satın alım tutarı ortalaması bağlamında bakıldığında en yüksek harcama ortalamasına sahip yaş grubu 51-55 yaş aralığı olarak tespit edilmiştir.

Anahtar Kelimeler

Satış Tahminlemesi, Makine Öğrenmesi, Regresyon Algoritmaları, Black Friday, Perakende Sektörü

Comparative Performance Analysis of Machine Learning Algorithms in the Retail Industry: Black Friday Sales Forecasting

Öz

The expansion of branch networks of large retail chains, the growth of their customer base, and the increasing diversity of customer profiles are exacerbating the complexity of sales forecasting processes. Managing this diversity and its implications presents a significant challenge for retailers in terms of both strategic planning and operational implementation. At this point, developing customer segmentation and personalized marketing strategies, determining unique approaches for each customer group, and effectively managing this diversity are becoming increasingly crucial. The emerging technologies, particularly machine learning methods, present the potential to cope with these challenges. In light of this, the main objective of the research is to perform sales forecasting on a retail company's Black Friday sales data using machine learning algorithms named Linear Regression, Random Forest Regression, K-Nearest Neighbors Regression, XGBoost Regression, Decision Tree Regression and LightGBM Regression and determine the best performing algorithm by comparing their performances. It is also aimed to tune the hyperparameters using GridSearchCV and examine the effect of these adjustments on the performance of the models. Additionally, Exploratory Data Analysis will be conducted on the dataset to create a sample example for businesses in the retail sector on how they can extract useful information from their available data and effectively evaluate it. According to the results obtained from the research, the most successful algorithm in predicting sales was the XGBoost Regression with hyperparameters tuned using GridSearchCV. It has been determined that the majority of the company's customers consist of individuals aged 26-35, with male customers making significantly higher purchases compared to females and single customers spending more than married ones. Furthermore, when examining the average amount of purchases made by each age group, it was identified that those within the range of 51-55 years had the highest average spending rate.

Anahtar Kelimeler

Sales Forecasting, Machine Learning, Regression Algorithms, Black Friday, Retail Industry

Kaynakça

• Abhinav, T., & Prasad, P. K. (2023). Black friday sales prediction using machine learning. UGC Care Group I Listed Journal, 13(11), 9-14.
• Aher, A., Rajeswari, K., & Vispute, S. (2021). Data Analysis and price prediction of black friday sales using machine learning technique. IJERT, 10(7), 621-627.
• Alagarsamy, S., Varma, K. G., Harshitha, K., Hareesh, K., & Varshini, K. (2023, January). Predictive Analytics for Black Friday Sales using Machine Learning Technique. In 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) (pp. 389-393). IEEE.
• Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: An overview. In Journal of Physics: Conference Series (Vol. 1142, p. 012012). IOP Publishing.
• Amjad, M., Ahmad, I., Ahmad, M., Wróblewski, P., Kamiński, P., & Amjad, U. (2022). Prediction of pile bearing capacity using XGBoost algorithm: modeling and performance evaluation. Applied Sciences, 12(4), 2126.
• Analytics Vidhya. (2016, July). Black friday sales prediction. https://datahack.analyticsvidhya.com/contest/black-friday
• Awan, M. J., Mohd Rahim, M. S., Nobanee, H., Yasin, A., & Khalaf, O. I. (2021). A big data approach to black friday sales. Intelligent Automation & Soft Computing, 27(3), 785-797.
• Belete, D. M., & Huchaiah, M. D. (2022). Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. International Journal of Computers and Applications, 44(9), 875-886.
• Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, 24.
• Beştaş, M. (2023). Keşifçi veri analizi ile eczane satış analizi ve satış tahmini. Third Sector Social Economic Review, 58(1), 765-782.
• Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222-2239.
• Bohanec, M., Borštnar, M. K., & Robnik-Šikonja, M. (2017). Explaining machine learning models in sales predictions. Expert Systems with Applications, 71, 416-428.
• Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
• Chen, C., Zhang, Q., Ma, Q., & Yu, B. (2019). LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion. Chemometrics and Intelligent Laboratory Systems, 191, 54-64.
• Chen, J., Koju, W., Xu, S., & Liu, Z. (2021, March). Sales forecasting using deep neural network and SHAP techniques. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 135-138). IEEE.
• Cheriyan, S., Ibrahim, S., Mohanan, S., & Treesa, S. (2018, August). Intelligent sales prediction using machine learning techniques. In 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (pp. 53-58). IEEE.
• Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.
• Çiçek, C. T., & Selçuk, G. D. (2023). Sanal market sektöründe hedef müşteri kitlesinin tanımlanması ve makine öğrenmesi ile tüketim eğilimlerinin tahmini. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(1), 24-35.
• de Oliveira, D., Porto, F., Boeres, C., & de Oliveira, D. (2021). Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning. Concurrency and Computation: Practice and Experience, 33(5), e5972.
• Dılkı, G. (2020). Makine öğrenmesi algoritmalarının sınıflama problemleri üzerinden karşılaştırılması: Satış tahmini. PressAcademia Procedia, 12(1), 82-83.
• Ecemiş, O., & Irmak, S. (2018). Paslanmaz çelik sektörü satış tahmininde veri madenciliği yöntemlerinin karşılaştırılması. Kilis 7 Aralık Üniversitesi Sosyal Bilimler Dergisi, 8(15), 148-169.
• Eker, R., Alkiş, K. C., Uçar, Z., Aydın, A. (2023). Ormancılıkta makine öğrenmesi kullanımı. Turkish Journal of Forestry, 24(2), 150-177. doi:10.18182/tjf.1282768
• Erol, B., & İnkaya, T. (2024). Satış tahmini için uzun kısa-süreli bellek ağı tabanlı derin transfer öğrenme yaklaşımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(1), 191-202.
• García, S., Luengo, J., & Herrera, F. (2016). Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowledge-Based Systems, 98, 1-29.
• Gilmore, E., Estivill-Castro, V., & Hexel, R. (2021). More interpretable decision trees. In Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021, Bilbao, Spain, September 22–24, 2021, Proceedings 16 (pp. 280-292). Springer International Publishing.
• Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., ... & Hussain, A. (2024). Interpreting blackbox models: a review on explainable artificial intelligence. Cognitive Computation, 16(1), 45-74.
• Herodotou, H., Odysseos, L., Chen, Y., & Lu, J. (2022, May). Automatic performance tuning for distributed data stream processing systems. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) (pp. 3194-3197). IEEE.
• Huang, C., Li, Y., & Yao, X. (2019). A survey of automatic parameter tuning methods for metaheuristics. IEEE Transactions on Evolutionary Computation, 24(2), 201-216.
• Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283-304.
• İyzico. (2022). 2022 iyzico Black Friday Karnesi.
• Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A., & Guizani, M. (2021). The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions. IEEE Internet of Things Journal, 9(15), 12861-12885.
• Jain, P., Choudhury, A., Dutta, P., Kalita, K., & Barsocchi, P. (2021). Random forest regression-based machine learning model for accurate estimation of fluid flow in curved pipes. Processes, 9(11), 2095.
• Kalra, S., Perumal, B., Yadav, S., & Narayanan, S. J. (2020, February). Analysing and predicting the purchases done on the day of Black Friday. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-8). IEEE.
• Kim, S. J., Bae, S. J., & Jang, M. W. (2022). Linear regression machine learning algorithms for estimating reference evapotranspiration using limited climate data. Sustainability, 14(18), 11674.
• Kohli, S., Godwin, G. T., & Urolagin, S. (2020). Sales prediction using linear and KNN regression. In Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019 (pp. 321-329). Singapore: Springer Singapore.
• Liao, W., Ye, G., Yin, Y., Yan, W., Ma, Y., & Zuo, D. (2020, November). Auto Parts Sales Prediction Based on Machine Learning for small data and a long replacement cycle. In 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-5). IEEE.
• Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.
• Mahendra, G., & Roopashree, H. R. (2023, February). Prediction of road accidents in the different states of India using machine learning algorithms. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-6). IEEE.
• Marr, B. (2016). Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. John Wiley & Sons.
• Mayer, J. H., Meinecke, M., Quick, R., Kusterer, F., & Kessler, P. (2022, December). Applying predictive analytics algorithms to support sales volume forecasting. In European, Mediterranean, and Middle Eastern Conference on Information Systems (pp. 63-76). Cham: Springer Nature Switzerland.
• Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116.
• Milo, T., & Somech, A. (2020, June). Automating exploratory data analysis via machine learning: An overview. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 2617-2622).
• Nacar, E. N., & Erdebilli, B. (2021). Makine öğrenmesi algoritmaları ile satış tahmini. Endüstri Mühendisliği, 32(2), 307-320.
• Niu, Y. (2020, October). Walmart sales forecasting using xgboost algorithm and feature engineering. In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) (pp. 458-461). IEEE.
• Özdemir, Ş., & Örslü, S. (2019). Makine öğrenmesinde yeni bir bakış açısı: Otomatik makine öğrenmesi (AutoML). Journal of Information Systems and Management Research, 1(1), 23-30.
• Patil, S., Nankar, O., Agrawal, R., Sharma, K., Awasthi, S., & Jha, N. (2023, January). Black Friday sales prediction using supervised machine learning. In 2023 International Conference on Artificial Intelligence and Smart Communication (AISC) (pp. 1006-1012). IEEE.
• Ramachandra, H. V., Balaraju, G., Rajashekar, A., & Patil, H. (2021, March). Machine learning application for black friday sales prediction framework. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 57-61). IEEE.
• Ranjan, G. S. K., Verma, A. K., & Radhika, S. (2019, March). K-nearest neighbors and grid search cv based real time fault monitoring system for industries. In 2019 IEEE 5th international conference for convergence in technology (I2CT) (pp. 1-5). IEEE.
• Sathya, R., & Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence, 2(2), 34-38.
• Selvi, G., Dag, G., Dirican, E. G., Aktay, T., Aksu, S. M., Özdem, K., ... Akcayol, M. A. (2021). Automated machine learning platform otomatik makine öğrenmesi platformu. 6th International Conference on Computer Science and Engineering, UBMK 2021 (ss.769-774). Ankara, Türkiye.
• Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised classification algorithms in machine learning: A survey and review. In Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018 (pp. 99-111). Springer Singapore.
• Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2021). Machine learning and artificial intelligence in haematology. British Journal of Haematology, 192(2), 239-250.
• Swilley, E., & Goldsmith, R. E. (2013). Black Friday and Cyber Monday: Understanding consumer intentions on two major shopping days. Journal of Retailing and Consumer Services, 20(1), 43-50
• Talkhi, N., Nooghabi, M. J., Esmaily, H., Maleki, S., Hajipoor, M., Ferns, G. A., & Ghayour-Mobarhan, M. (2023). Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique. Scientific Reports, 13(1), 12775.
• Thomas, T., P. Vijayaraghavan, A., Emmanuel, S., Thomas, T., P. Vijayaraghavan, A., & Emmanuel, S. (2020). Applications of decision trees. Machine Learning Approaches in Cyber Security Analytics, 157-184.
• Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1-20.
• Trung, N. D., Thien, T. D., Luu, T. D., & Huynh, H. X. (2021, July). Black Friday sale prediction via extreme gradient boosted trees. In Proceedings of the 12th National Conference on Basic and Applied Research in Information Technology (FAIR) (pp. 49-57). Acesso em.
• Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia-Social and Behavioral Sciences, 106, 234-240.
• Wang, R., Wang, L., Zhang, J., He, M., & Xu, J. (2022). XGBoost machine learning algorism performed better than regression models in predicting mortality of moderate-to-severe traumatic brain injury. World Neurosurgery, 163, e617-e622.
• Wang, Z., & Bovik, A. C. (2009). Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine, 26(1), 98-117.
• Wu, C. S. M., Patil, P., & Gunaseelan, S. (2018, November). Comparison of different machine learning algorithms for multiple regression on black friday sales data. In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) (pp. 16-20). IEEE.
• Xia, Z., Xue, S., Wu, L., Sun, J., Chen, Y., & Zhang, R. (2020). ForeXGBoost: Passenger car sales prediction based on XGBoost. Distributed and Parallel Databases, 38, 713-738.
• Yalçın, F. G. (2022). Craftgate, Kasım Ayı İndirimlerine İlişkin Online Alışveriş Verilerini Açıkladı.
• Zeng, M., Cao, H., Chen, M., & Li, Y. (2019). User behaviour modeling, recommendations, and purchase prediction during shopping festivals. Electronic Markets, 29, 263-274.
• Zhang, D., & Gong, Y. (2020). The comparison of LightGBM and XGBoost coupling factor analysis and prediagnosis of acute liver failure. IEEE Access, 8, 220990-221003.
• Zhu, X., Chu, J., Wang, K., Wu, S., Yan, W., & Chiam, K. (2021). Prediction of rockhead using a hybrid N-XGBoost machine learning framework. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1231-1245.