Browse > Article

A Study on Predictive Modeling of Public Data: Survival of Fried Chicken Restaurants in Seoul  

Bang, Junah (성균관대학교 통계학과)
Son, Kwangmin (성균관대학교 통계학과)
Lee, So Jung Ashley (CJ올리브네트웍스 DT융합연구소)
Lee, Hyeongeun (CJ올리브네트웍스 빅데이터센터)
Jo, Subin (성균관대학교 통계학과)
Publication Information
The Journal of Bigdata / v.3, no.2, 2018 , pp. 35-49 More about this Journal
Abstract
It seems unrealistic to say that fried chicken, often known as the American soul food, has one of the biggest markets in South Korea. Yet, South Korea owns more numbers of fried chicken restaurants than those of McDonald's franchise globally[4]. Needless to say not all these fast-food commerce survive in such small country. In this study, we propose a predictive model that could potentially help one's decision whilst deciding to open a store. We've extracted all fried chicken restaurants registered at the Korean Ministry of the Interior and Safety, then collected a number of features that seem relevant to a store's closure. After comparing the results of different algorithms, we conclude that in order to best predict a store's survival is FDA(Flexible Discriminant Analysis). While Neural Network showed the highest prediction rate, FDA showed better balanced performance considering sensitivity and specificity.
Keywords
Entrepreneurship; Restaurant; Survival; Machine Learning; Predictive Model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 고은지, "소상공인 71%, 5년내 문 닫아...식당.여관은 1년내 절반 폐업", 2016.09.28., https://www.yna.co.kr/view/AKR20160927179000003
2 국가법령정보센터, 국토의 계획 및 이용에 관한 법률, 2018.06.12.
3 국가법령정보센터, 다중이용업소의 안전관리에 관한 특별법, 2017.12.26.
4 김현우, "'우후죽순' 치킨집, 전 세계 맥도날드 매장보다 많아", 2015.10.05., https://www.ytn.co.kr/_ln/0102_201510052201553904
5 박수호, & 서은내, "3040 vs 5060 세대별 창업 특징 살펴보니 | "인생 이모작…내 노후는 내가" 5060 반란", 2016.08.12., http://news.mk.co.kr/newsRead.php?no=575619&year=2016
6 배정원, "소비자기대지수", 2012.11.24., http://biz.chosun.com/site/data/html_dir/2012/11/24/2012112400390.html
7 윤효원, "자영업자 700만명, 절반으로 줄여야", 2018.09.03., http://www.labortoday.co.kr/news/articleView.html?idxno=153665
8 이진욱, 유국현, 문병민, 배석주, "감성분석과 Word2vec을 이용한 비정형 품질 데이터 분석", 품질경영학회지, 제45권, 제1호, pp.117-127, 2017.   DOI
9 이현, "창업자 5명 중 2명은 치킨집.편의점...이미 포화 상태", 2016.07.11., http://news.jtbc.joins.com/article/article.aspx?news_id=NB11269730&pDate=20160711
10 최현준, "선행종합지수", 2012.03.04., http://www.hani.co.kr/arti/economy/economy_general/521840.html
11 통계청, 기업생멸행정통계, 2016.
12 통계청, 자영업 현황분석, 2016.
13 "Orthogonal Partial Least Squares (OPLS) in R", 2013.07.28., https://www.r-bloggers.com/orthogonal-partial-least-squares-opls-in-r
14 "A Quick Introduction to K-Nearest Neighbors Algorithm", 2017.04.11., https://medium.com/@adi.bronshtein/a-quick-introduction-to-k-nearest-neighbors-algorithm-62214cea29c7
15 "SVM Separating Hyperplanes", 2012.11.26., https://en.wikipedia.org/wiki/Support_vector_machine#cite_note-CorinnaCortes-1/512px-Svm_separating_hyperplanes_(SVG).svg
16 "What is an artificial neural network? Here's everything you need to know", 2018.09.13, https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/
17 Chen, T., & Guestrin, C., "XGBoost: A Scalable Tree Boosting System", International Conference on Knowledge Discovery and Data Mining, pp.785-794, 2016.
18 Fawcett, Tom, "An Introduction to ROC Analysis", Pattern Recognition Letters, Vol.27, No.8, pp.861-874, 2006.   DOI
19 Friedman J, Hastie T, Tibshirani R., "Additive Logistic Regression: A Statistical View of Boosting", Annals of Statistics, Vol.28, No.2, pp.337-374, 2000.   DOI
20 Han, J., & Kamber, M., Data mining: Concepts and techniques (3rd ed.), Amsterdam: Elsevier, Morgan Kaufmann, 2011.
21 Hastie, T., Tibshirani, R., & Buja, A., "Flexible Discriminant Analysis by Optimal Scoring", J. of the American Statistical Association, Vol.89, No.428, pp.1255-1270, 1994.   DOI
22 Leo Breiman, "Random Forests", 2001., https://www.stat.berkeley.edu/-breiman/randomforest2001.pdf
23 Hoerl, A., & Kennard, R., "Ridge Regression: Biased Estimation for Nonorthogonal Problems", Technometrics, Vol. 42, No. 1, pp.80-86, 2000.   DOI
24 Keller, J. M., Gray, M. R., & Givens, J. A., "A fuzzy K-nearest neighbor algorithm", IEEE Transactions on Systems, Man, and Cybernetics, Vol.SMC-15, No.4, pp.580-585, 1985.   DOI
25 Kuhn, M., & Johnson, K., Applied predictive modeling (2nd ed.), New York: Springer., 2016.
26 Schalkoff, R.J, Artificial neural networks, McGraw-Hill, 1997.
27 Sinnott, R.W, "Virtues of the Haversine", Sky and Telescope, Vol. 68, Issue 2, pp.158, 1984.
28 Vapnik, V. N., The nature of statistical learning theory, New York: Springer, 2010.
29 Wold, S., Sjostrom, M., & Erikssonb, L., "PLS-regression: A basic tool of chemometrics", Chemometrics and Intelligent Laboratory Systems, Vol.58, No.2, pp.109-130, 2001.   DOI
30 Zou, H., & Hastie, T., "Regularization and Variable Selection via the Elastic Net", J. of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 67, No.2, pp.301-320. 2005.   DOI