This paper is published in Volume-6, Issue-5, 2020
Area
Data Science
Author
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu
Org/Univ
Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India
Keywords
Big Data Analysis, E-Commerce, Predicting.
Citations
IEEE
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu. E-commerce data analysis to find online shopping trend, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu (2020). E-commerce data analysis to find online shopping trend. International Journal of Advance Research, Ideas and Innovations in Technology, 6(5) www.IJARIIT.com.
MLA
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu. "E-commerce data analysis to find online shopping trend." International Journal of Advance Research, Ideas and Innovations in Technology 6.5 (2020). www.IJARIIT.com.
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu. E-commerce data analysis to find online shopping trend, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu (2020). E-commerce data analysis to find online shopping trend. International Journal of Advance Research, Ideas and Innovations in Technology, 6(5) www.IJARIIT.com.
MLA
Deva Dharshini D., Mani Barathi Sp S., Rekha V. S., Dr. M. Sujithra, Dr.P. Velvadivu. "E-commerce data analysis to find online shopping trend." International Journal of Advance Research, Ideas and Innovations in Technology 6.5 (2020). www.IJARIIT.com.
Abstract
E-commerce has transformed the way business is done in India. There are expected to be over 2B digital buyers in the world in 2020.Share in ecommerce market is 1.6% but revenue will increase 51% in near future. By 2040, around 95% of all purchases are expected to be via ecommerce. This paper presents an overview of the unique features that differentiate big data from traditional datasets. In addition, to discover trends in online shopping of users predicting whether the user will make revenue/not based on user characteristics and also to find customized offers based on user shopping data history and categorizing them.
