An Online Retail Market Analysis for Social Development with Machine Learning
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Published
Oct 22, 2021
Prof.(Dr.) Bhavana Narain
Dr. Manjushree Nayak
Abstract
Present era is a digital era where retail marketing & online marketing plays an important role in people living style. Filling the gap between customer &market is a technological responsibility of technocrat’s. In our work we have collected online data and retail data of last 5 years. These data were collected from two major organizations which deal with online marketing and retail marketing. Techniques from unsupervised data type were implemented to analyze the collected data. Knowledge gain from this analysis is used for marketing upliftment & social development. New modified K mean clustering Algorithm (NMKMCA) is used for data analysis. Accuracy result of retail marketing& online marketing is compared in our work. We have taken I/O time and computational time as our working parameters. Result of this parameters are analyzed & discussed in our work. In last section of our work we find that NMKMCA will take less time in computing very large dataset.
How to Cite
Narain, P. B., & Nayak, D. M. (2021). An Online Retail Market Analysis for Social Development with Machine Learning. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/70
Article Details
Keywords
Retail, Online, Machine Learning, Clustering, Unsupervised Learning
References
[1] Nayak Manjushree (2017) PRELIMS OF R, “International Journal of Technical Research and Applications”, e-ISSN: 2320-8163, www.ijtra.com Volume 5, Issue 1 (Jan Feb 2017), pp 35-37.
[2] Praveen Kumari, Hakam Singh, Pratibha Sharma, 2017,” Computational Time Analysis of K-mean Clustering Algorithm”, International Journal of Engineering Development and Research (IJEDR), Volume 5, Issue 2, ISSN: 2321-9939, pp 1481-1486.
[3] McAfee, R. P., & TeVelde, V. (2006). Dynamic pricing in the airline industry.forthcoming Aranoff, G.(2017), “Globalization alternative pricing in a peak-load pricing model” Moden economy,8, pp 888-896.
[4] UthayasankarSivarajah ⁎, Muhammad Mustafa Kamal, ZahirIrani, VishanthWeerakkody(2017),” Critical analysis of Big Data challenges and analytical methods”, Journal of Business Research 70, pp-263–286.
[5] Agarwal, R., &Dhar, V. (2014). Editorial – big data, data science, and analytics: the opportunity and challenge for is research. Information Systems Research, 25(3), pp 443–448
[6] SayaliGaikwad ; PranaliNale ; RavindraBachate(2016)”, Survey on Big data Analytics for digital world” , IEEE International Conference,pp-180 – 186
[7] Manyika J, Michael C, Brown B, et al. Mc Kinsey; May 2011. “Big data: The next frontier for innovation, competition, and productivity”, Mv Kinsey Global institute, pp1-13.
[8] Manyika J, Chui M, Brown B, et al. Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute. 2011
[9] Ahrens J, Hendrickson B, Long G, Miller S, Ross R, Williams D. Data-intensive science in the US DOE: case studies and future challenges. Computing in Science and Engineering. 2011;13(6):pp 14– 23.
[10] Hey T, Tansley S, Tolle K. The Fourth Paradigm: Data-Intensive Scientific Discovery. 2009. Kim NS, Austin T, Blaauw D, et al. Leakage current: Moore's law meets static power. Computer. 2003;36(12): pp 68–75.
[11] Labrinidis A, Jagadish H. Challenges and opportunities with big data. Proceedings of the VLDB Endowment. 2012;5(12):pp 2032–2033.
[12] Kouzes RT, Anderson GA, Elbert ST, Gorton I, Gracio DK. The changing paradigm of data-intensive computing. IEEE Computer. 2009;42(1):pp 26–34
[13] Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. Morgan Kaufmann; 2006.
[14] Ashish kumar singh& grace rumantir,”clustering Experiments on Big transaction Data for market segmentation”Proceedings of the international conference on bigdata science and computing,Article No-16,2014
[15] GarimaSehgal, Dr. KanwalGarg,”Comparison of Various Clustering Algorithms”,(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, pp 3074-3076.
[16] Alba, Joseph&Lynch, John&Weitz, Barton,Janiszewski,Chris Lutz(1997)” Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces” Journal of Marketing Vol. 61 ,pp 38-53.
[17] ArpitBansal et.al(2017), “Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining“,International Journal of Computer Applications (0975 – 8887) Volume 157 – No 6, January 2017.
[18] André Ribeiro, et.al,(2015)”Data Modeling and Data Analytics: A Survey from a Big Data Perspective”volume-Journal of Software Engineering and Applications, 2015, 8, 617-634 Published Online December 2015 in SciRes.
[19] Antonakakis, Nikolaos&Floros, Christos Kizys(2016)” Dynamic spillover effects in futures markets: UK and US evidence” International Review of Financial Analysis,Elsevier, vol. 48(C), pp 406-418.
[20] Adarsh V. Rotte et.al,(2014) “Big Data Analytics Made Easy With Rhadoop”volume-IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, pp-.11-26
[21] AmitSaha,(2015),”A Study on “The impact of online shopping upon retail trade business”,IOSR Journal of Business and Management (IOSR-JBM) e-ISSN: 2278-487X, p-ISSN: 2319-7668. pp 74-78.
[22] Agarwal, R., &Dhar, V. (2014). Editorial – big data, data science, and analytics: the opportunity and challenge for is research. Information Systems Research, 25(3), pp 443–448.
[23] 27.Ahrens J, Hendrickson B, Long G, Miller S, Ross R, Williams D. Data-intensive science in the USDOE: case studies and future challenges. Computing in Science and Engineering. 2011;13(6):pp 14–23.
[24] Aranoff, G.(2017), “Globalization alternative pricing in a peak-load pricing model” ,Moden economy,8,pp 888-896.
[2] Praveen Kumari, Hakam Singh, Pratibha Sharma, 2017,” Computational Time Analysis of K-mean Clustering Algorithm”, International Journal of Engineering Development and Research (IJEDR), Volume 5, Issue 2, ISSN: 2321-9939, pp 1481-1486.
[3] McAfee, R. P., & TeVelde, V. (2006). Dynamic pricing in the airline industry.forthcoming Aranoff, G.(2017), “Globalization alternative pricing in a peak-load pricing model” Moden economy,8, pp 888-896.
[4] UthayasankarSivarajah ⁎, Muhammad Mustafa Kamal, ZahirIrani, VishanthWeerakkody(2017),” Critical analysis of Big Data challenges and analytical methods”, Journal of Business Research 70, pp-263–286.
[5] Agarwal, R., &Dhar, V. (2014). Editorial – big data, data science, and analytics: the opportunity and challenge for is research. Information Systems Research, 25(3), pp 443–448
[6] SayaliGaikwad ; PranaliNale ; RavindraBachate(2016)”, Survey on Big data Analytics for digital world” , IEEE International Conference,pp-180 – 186
[7] Manyika J, Michael C, Brown B, et al. Mc Kinsey; May 2011. “Big data: The next frontier for innovation, competition, and productivity”, Mv Kinsey Global institute, pp1-13.
[8] Manyika J, Chui M, Brown B, et al. Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute. 2011
[9] Ahrens J, Hendrickson B, Long G, Miller S, Ross R, Williams D. Data-intensive science in the US DOE: case studies and future challenges. Computing in Science and Engineering. 2011;13(6):pp 14– 23.
[10] Hey T, Tansley S, Tolle K. The Fourth Paradigm: Data-Intensive Scientific Discovery. 2009. Kim NS, Austin T, Blaauw D, et al. Leakage current: Moore's law meets static power. Computer. 2003;36(12): pp 68–75.
[11] Labrinidis A, Jagadish H. Challenges and opportunities with big data. Proceedings of the VLDB Endowment. 2012;5(12):pp 2032–2033.
[12] Kouzes RT, Anderson GA, Elbert ST, Gorton I, Gracio DK. The changing paradigm of data-intensive computing. IEEE Computer. 2009;42(1):pp 26–34
[13] Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. Morgan Kaufmann; 2006.
[14] Ashish kumar singh& grace rumantir,”clustering Experiments on Big transaction Data for market segmentation”Proceedings of the international conference on bigdata science and computing,Article No-16,2014
[15] GarimaSehgal, Dr. KanwalGarg,”Comparison of Various Clustering Algorithms”,(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, pp 3074-3076.
[16] Alba, Joseph&Lynch, John&Weitz, Barton,Janiszewski,Chris Lutz(1997)” Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces” Journal of Marketing Vol. 61 ,pp 38-53.
[17] ArpitBansal et.al(2017), “Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining“,International Journal of Computer Applications (0975 – 8887) Volume 157 – No 6, January 2017.
[18] André Ribeiro, et.al,(2015)”Data Modeling and Data Analytics: A Survey from a Big Data Perspective”volume-Journal of Software Engineering and Applications, 2015, 8, 617-634 Published Online December 2015 in SciRes.
[19] Antonakakis, Nikolaos&Floros, Christos Kizys(2016)” Dynamic spillover effects in futures markets: UK and US evidence” International Review of Financial Analysis,Elsevier, vol. 48(C), pp 406-418.
[20] Adarsh V. Rotte et.al,(2014) “Big Data Analytics Made Easy With Rhadoop”volume-IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, pp-.11-26
[21] AmitSaha,(2015),”A Study on “The impact of online shopping upon retail trade business”,IOSR Journal of Business and Management (IOSR-JBM) e-ISSN: 2278-487X, p-ISSN: 2319-7668. pp 74-78.
[22] Agarwal, R., &Dhar, V. (2014). Editorial – big data, data science, and analytics: the opportunity and challenge for is research. Information Systems Research, 25(3), pp 443–448.
[23] 27.Ahrens J, Hendrickson B, Long G, Miller S, Ross R, Williams D. Data-intensive science in the USDOE: case studies and future challenges. Computing in Science and Engineering. 2011;13(6):pp 14–23.
[24] Aranoff, G.(2017), “Globalization alternative pricing in a peak-load pricing model” ,Moden economy,8,pp 888-896.
Section
GE3- Computers & Information Technology