Visualization of Optimal Product Pricing using E-Commerce Data
N Greeshma1, C Raghavendra2, K Rajendra Prasad3
1N Greeshma*, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
2C Raghavendra*, Asst. Professor, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
3Dr. K Rajendra Prasad*, Professor and Head, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4441-4443 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5262119119/2019©BEIESP | DOI: 10.35940/ijitee.A5262.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the number of e-commerce websites being increasing rapidly, online shopping has become the trend nowadays. Though, online shopping is very easy; however, when it comes to product selection it is a tedious task and time consuming to identify which online site gives us the best price and offers. Comparing products and filtering them from each online site is a very time consuming task for a buyer. This paper uses the techniques of Web Scraping using python libraries like Beautiful Soup, requests, matplotlib for identifying the best prices and for deciding the best product deal to the customer from different online websites. Web scraping is an automated technique of extracting data from websites. In this paper, real time data is extracted from various e-commerce sites and compared automatically. Finally, the results are graphically displayed based on which the customer makes the appropriate decision.
Keywords: Web Scraping, e-Commerce Data Extraction, Python Libraries.
Scope of the Article: e-Commerce