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Machine Learning Algorithms for Industrial Applications pp 209–235Cite as

An Application of Operational Analytics: For Predicting Sales Revenue of Restaurant

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 907))

Abstract

Operational analytics improves existing operations of a firm by focusing on process improvement. It acts a business tool for resource management, data streamlining which improves productivity, employee engagement, customer satisfaction and provide investment opportunities. Crucial insights into the problem can be obtained which aids to determine key business strategy through various stages of data analysis and modeling, such as exploratory data analysis, predictive modeling, documentation and reporting. In this work, a real world dataset is considered for the study, where the sales revenue of restaurant is predicted. A second stage regression model built upon base regression models which are linear regression, ridge regression, decision tree regressor. Based on the results obtained, the following findings are reported: (i) annual sales revenue trend, (ii) food preference in cities, (iii) demand variability i.e. effect of first week and weekend, and (iv) comparison against ensemble methods in terms of prediction accuracy. This work also suggest avenues for future research in this direction.

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References

  1. Tufféry, S. (2011). Data Mining and Statistics for Decision Making. Hoboken: Wiley.

    Google Scholar 

  2. Mosteller, F., Tukey, J. W., et al. (1977). Data Analysis and Regression: A Second Course in Statistics. Reading: Addison-Wesley.

    Google Scholar 

  3. Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (1983). Understanding Robust and Exploratory Data Analysis (Vol. 3). New York: Wiley.

    MATH  Google Scholar 

  4. Cui, Z., Badam, S. K., Adil Yalçin, M., & Elmqvist, N. (2019). Datasite: proactive visual data exploration with computation of insight-based recommendations. Information Visualization, 18(2), 251–267.

    Article  Google Scholar 

  5. Pabinger, S., Dander, A., Fischer, M., Snajder, R., Sperk, M., Efremova, M., Krabichler, B., Speicher, M. R., Zschocke, J., & Trajanoski, Z. (2014). A survey of tools for variant analysis of next-generation genome sequencing data. Briefings in Bioinformatics, 15(2), 256–278.

    Google Scholar 

  6. Rautenhaus, M., Böttinger, M., Siemen, S., Hoffman, R., Kirby, R. M., Mirzargar, M., Röber, N., & Westermann, R. (2017). Visualization in meteorology a survey of techniques and tools for data analysis tasks. IEEE Transactions on Visualization and Computer Graphics, 24(12), 3268–3296.

    Google Scholar 

  7. Endert, A., Ribarsky, W., Turkay, C., William Wong, B.L., Nabney, I., Díaz Blanco, I., & Rossi, F. (2017). The state of the art in integrating machine learning into visual analytics. In Computer Graphics Forum (Vol. 36, pp. 458–486). Wiley Online Library

    Google Scholar 

  8. Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics, 1(1), 48–56.

    Article  Google Scholar 

  9. Zhang, M.-L., & Zhou, Z.-H. (2006). Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 18(10), 1338–1351.

    Article  Google Scholar 

  10. Idreos, S., Papaemmanouil, O., & Chaudhuri, S. (2015). Overview of data exploration techniques. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 277–281).

    Google Scholar 

  11. Khan, M., & Khan, S. S. (2011). Data and information visualization methods, and interactive mechanisms: A survey. International Journal of Computer Applications, 34(1), 1–14.

    Article  Google Scholar 

  12. Godfrey, P., Gryz, J., & Lasek, P. (2016). Interactive visualization of large data sets. IEEE Transactions on Knowledge and Data Engineering, 28(8), 2142–2157.

    Article  Google Scholar 

  13. Dey, N., Ashour, A. S., Shi, F., Fong, S. J., & Simon Sherratt, R. (2017). Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Transactions on Consumer Electronics, 63(4), 442–449.

    Article  Google Scholar 

  14. Elhayatmy, G., Dey, N., & Ashour, A.S. (2018). Internet of things based wireless body area network in healthcare. In Internet of Things and Big Data Analytics Toward Next-generation Intelligence (pp. 3–20). Springer.

    Google Scholar 

  15. Das, S. K., & Tripathi, S. (2018). Adaptive and intelligent energy efficient routing for transparent heterogeneous ad-hoc network by fusion of game theory and linear programming. Applied Intelligence, 48(7), 1825–1845.

    Article  Google Scholar 

  16. Das, S. K., & Tripathi, S. (2019). Energy efficient routing formation algorithm for hybrid ad-hoc network: A geometric programming approach. Peer-to-Peer Networking and Applications, 12(1), 102–128.

    Article  Google Scholar 

  17. Santosh Kumar, D., Sourav, S., & Nilanjan, D., et al. (2020). Design frameworks for wireless networks. In Lecture Notes in Networks and Systems, Springer (pp. 1–439)

    Google Scholar 

  18. Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.

    Article  Google Scholar 

  19. Gibbs, W. J. (2015). Contemporary Research Methods and Data Analytics in the News Industry. Hershey: IGI Global.

    Book  Google Scholar 

  20. Bro, R., van den Berg, F., Thybo, A., Andersen, C. M., Jørgensen, B. M., & Andersen, H. (2002). Multivariate data analysis as a tool in advanced quality monitoring in the food production chain. Trends in Food Science & Technology, 13(6–7), 235–244.

    Article  Google Scholar 

  21. Hey, T., Tansley, S., Tolle, K., et al. (2009). The Fourth Paradigm: Data-intensive Scientific Discovery (Vol. 1). RedmondRedmond: Microsoft Research.

    Google Scholar 

  22. Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2009). Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning. Information Fusion, 10(4), 354–363.

    Article  Google Scholar 

  23. Zheng, A., & Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. Sebastopol: O’Reilly Media Inc.

    Google Scholar 

  24. Brandt, S. (1976). Statistical and computational methods in data analysis. Technical report. Amsterdam: North-Holland Publishing Company.

    Google Scholar 

  25. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.

    Article  Google Scholar 

  26. Coates, A., Ng, A., & Lee, H. (2011). An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 215–223).

    Google Scholar 

  27. Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.

    Article  MathSciNet  MATH  Google Scholar 

  28. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.

    Article  MathSciNet  MATH  Google Scholar 

  29. Sanguansat, P. (2012). Principal Component Analysis: Multidisciplinary Applications. Norderstedt: BoD-Books on Demand.

    Book  Google Scholar 

  30. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607–609.

    Article  Google Scholar 

  31. Yu, H.-F., Lo, H.-Y., Hsieh, H.-P., Lou, J.-K., McKenzie, T.G., Chou, J.-W., Chung, P.-H., Ho, C.-H., Chang, C.-F., & Wei, Y.-H., et al. (2010). Feature engineering and classifier ensemble for KDD cup 2010. In KDD Cup.

    Google Scholar 

  32. Zhao, Z., Morstatter, F., Sharma, S., Alelyani, S., Anand, A., & Liu, H. (2010). Advancing feature selection research. ASU Feature Selection Repository, 1–28.

    Google Scholar 

  33. Langley, P., et al. (1994). Selection of relevant features in machine learning. In Proceedings of the AAAI Fall Symposium on Relevance (Vol. 184, pp. 245–271).

    Google Scholar 

  34. Khotanzad, A., & Hong, Y. H. (1990). Rotation invariant image recognition using features selected via a systematic method. Pattern Recognition, 23(10), 1089–1101.

    Article  Google Scholar 

  35. Goltsev, A., & Gritsenko, V. (2012). Investigation of efficient features for image recognition by neural networks. Neural Networks, 28, 15–23.

    Article  Google Scholar 

  36. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2013). A simultaneous feature adaptation and feature selection method for content-based image retrieval systems. Knowledge-Based Systems, 39, 85–94.

    Article  Google Scholar 

  37. Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361–397.

    Google Scholar 

  38. Van Landeghem, S., Abeel, T., Saeys, Y., & Van de Peer, Y. (2010). Discriminative and informative features for biomolecular text mining with ensemble feature selection. Bioinformatics, 26(18), i554–i560.

    Article  Google Scholar 

  39. Song, Q., Ni, J., & Wang, G. (2011). A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, 25(1), 1–14.

    Article  Google Scholar 

  40. Gibert, J., Valveny, E., & Bunke, H. (2012). Feature selection on node statistics based embedding of graphs. Pattern Recognition Letters, 33(15), 1980–1990.

    Article  Google Scholar 

  41. Li, H., Li, C.-J., Xian-Jun, W., & Sun, J. (2014). Statistics-based wrapper for feature selection: An implementation on financial distress identification with support vector machine. Applied Soft Computing, 19, 57–67.

    Article  Google Scholar 

  42. Morgan, B. J. T. (2001). Model selection and inference: A practical information-theoretic approach. Biometrics, 57(1), 320.

    Google Scholar 

  43. Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5, 1531–1555.

    MathSciNet  MATH  Google Scholar 

  44. Xu, Z., King, I., Lyu, M. R.-T., & Jin, R. (2010). Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural networks, 21(7), 1033–1047.

    Article  Google Scholar 

  45. Swiniarski, R. W., & Skowron, A. (2003). Rough set methods in feature selection and recognition. Pattern Recognition Letters, 24(6), 833–849.

    Article  MATH  Google Scholar 

  46. Derrac, J., Cornelis, C., García, S., & Herrera, F. (2012). Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Information Sciences, 186(1), 73–92.

    Article  Google Scholar 

  47. Graybill, F. A. (1976). Theory and Application of the Linear Model (Vol. 183). North Scituate: Duxbury Press.

    MATH  Google Scholar 

  48. Segerstedt, B. (1992). On ordinary ridge regression in generalized linear models. Communications in Statistics-Theory and Methods, 21(8), 2227–2246.

    Article  MathSciNet  MATH  Google Scholar 

  49. Singh, S., & Gupta, P. (2014). Comparative study Id3, cart and C4. 5 decision tree algorithm: A survey. International Journal of Advanced Information Science and Technology (IJAIST), 27(27), 97–103.

    Google Scholar 

  50. Rasoul Safavian, S., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674.

    Article  MathSciNet  Google Scholar 

  51. Viaene, S., Derrig, R. A., Baesens, B., & Dedene, G. (2002). A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. Journal of Risk and Insurance, 69(3), 373–421.

    Article  Google Scholar 

  52. Sebban, M., Mokrousov, I., Rastogi, N., & Sola, C. (2002). A data-mining approach to spacer oligonucleotide typing of mycobacterium tuberculosis. Bioinformatics, 18(2), 235–243.

    Article  Google Scholar 

  53. Yong, Z., Youwen, L., & Shixiong, X. (2009). An improved knn text classification algorithm based on clustering. Journal of Computers, 4(3), 230–237.

    Google Scholar 

  54. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892.

    Article  MATH  Google Scholar 

  55. Bera, S., Chattopadhyay, M., & Dan, P. K. (2018). A two-stage novel approach using centre ordering of vectors on agglomerative hierarchical clustering for manufacturing cell formation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(14), 2651–2662.

    Article  Google Scholar 

  56. Polikar, R. (2012). Ensemble learning. In Ensemble Machine Learning (pp. 1–34). Springer.

    Google Scholar 

  57. Liu, Y., Yao, X., & Higuchi, T. (2000). Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation, 4(4), 380–387.

    Article  Google Scholar 

  58. Breiman, L. (2000). Randomizing outputs to increase prediction accuracy. Machine Learning, 40(3), 229–242.

    Article  MATH  Google Scholar 

  59. Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation forest: a new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1619–1630.

    Article  Google Scholar 

  60. Granitto, P. M., Verdes, P. F., & Alejandro Ceccatto, H. (2005). Neural network ensembles: Evaluation of aggregation algorithms. Artificial Intelligence, 163(2), 139–162.

    Article  MathSciNet  MATH  Google Scholar 

  61. Bühlmann, P. (2010). Handbook of computational statistics: concepts and methods, chapter bagging, boosting and ensemble methods.

    Google Scholar 

  62. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Article  MATH  Google Scholar 

  63. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

    Article  MATH  Google Scholar 

  64. Breiman, L. (1996). Stacked regressions. Machine Learning, 24(1), 49–64.

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Indian Institute of Technology (Indian School of Mines) Dhanbad, Police Line Rd, Sardar Patel Nagar, Dhanbad, 826004, Jharkhand, India

    Samiran Bera

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  1. Samiran Bera
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Correspondence to Samiran Bera .

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Editors and Affiliations

  1. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

    Santosh Kumar Das

  2. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

    Shom Prasad Das

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Founder and Head of the Egyptian Scientific Research Group (SRGE), Information Technology Department, Cairo University, Faculty of Computer and Artificial Intelligence, Giza, Egypt

    Aboul-Ella Hassanien

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Bera, S. (2021). An Application of Operational Analytics: For Predicting Sales Revenue of Restaurant. In: Das, S., Das, S., Dey, N., Hassanien, AE. (eds) Machine Learning Algorithms for Industrial Applications. Studies in Computational Intelligence, vol 907. Springer, Cham. https://doi.org/10.1007/978-3-030-50641-4_13

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