the paper entitled as “Scraping and Visualization of Product Data from E-commerce Websites”. Extracting data from websites is called as web scrapping. The main advantages of the scrapping are inexpensive, easy to implement, low maintenance and speed. The main objective of the work is to scrap the data from websites and store the extracted data in Comma-separated values (CSV) format for analysis. The data available in the websites are in the form of unstructured information. Web scraping helps to collect these unstructured data and store it in a structured form. The process of Web scraping is to extract the data using various methods from the internet. Millions of people consider universally accessible resource as internet. The rise in the usage of internet has commonly been increased day by day and there is high growth in competition between the organizations in their business. This work consists with three phases. The first phase of the work is web scrapping. In this phase, the extracted data will be stored as a csv file. The second phase of the work is data analysis. In this phase, the data is imported from the csv format and analyzed using statistical analysis. The third phase of the work is visualization and in this the extracted data has been visualized with the help of different charts.

Web scrapping, Data Analysis, Visualization, data mining, websites

[1] Eloisa Vargiu, Mirko Urru, “Exploiting web scraping in a collaborative filteringbased approach to web advertising”, Artificial Intelligence Research, Vol. 2, Issue 1, pp. 44-54, 2013.
[2] Vasani Krunal A., “Content Evocation Using Web Scraping and Semantic Illustration”, IOSR Journal of Computer Engineering (IOSR-JCE) Vol. 16, Issue 3, pp. 54-60, 2014
[3] Jose Ignacio Fern ´ andez-Villamor, Jacobo Blasco-Garc ´ ´ıa, Carlos A. Iglesias, Mercedes Garijo, “A Semantic Scraping Model for Web Resources-Applying Linked Data to Web Page Screen” in the Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Volume 2 - Agents, Rome, Italy, 2011.
[4] Emilio Ferraraa, Pasquale De Meob, Giacomo Fiumarac, Robert Baumgartner, “Web Data Extraction, Applications and Techniques: A Survey”, Elsevier, knowledge-Based Systems, pp. 301-323, 2014.
[5] Faustina Johnson and Santosh Kumar Gupta. “Web Content Mining Techniques: A Survey”, International Journal of Computer Applications Vol. 47, Issue 11, pp. 44-50, 2012 .
[6] Rahul Dhawani, Mrudav Shukla, Priyanka Puvar, Bhagirath Prajapati, “A Novel Approach to Web Scraping Technology”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5, Issue 5, pp.747-750, 2015
[7] Anand V.Saukar, Kedar G.Pathare, Shweta A. Gode, “An Overview on Web Scrapping Techniques and Tools”, International Journal on Future Revolution in Compuer Science & Communication Engineering, Vol. 4, Issue 4, 2018.
[8] Riya Shah, Karishma Pathan, Anand Masurkar, Shweta Rewatkar, Prof. (Ms.) P.N.Vengurlekar, “Comparison of E-commerce Products using web mining”, International Journal of Scientific and Research Publications, Vol. 6, Issue 5,pp.640-644, 2016.
[9] Nakash, J., Anas, S., Ahmad, S. M., Azam, A. M., Khan, T. “Real Time Product Analysis using Data Mining” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 4, Issue 3, pp. 815–820, 2015.
[10] Rehman S.U. ‘Smart agent for automated E-commerce’, 2011 World Congress on Sustainable Technologies (WCST), UK IEEE pp.124-128, 2011.
[11] Shikha Mahajan, Nikhit Kumar , “A Web Scraping Approach in Node.js”, International Journal of Science, Engineering and Technology Research (IJSETR) Vol. 4, Issue 4, pp. 909-912, 2015.
[12] Sneh Nain, Bhumika Lall, “Web Data Scraper Tools: Survey”, International Journal of Computer Science and Engineering, Vol. 2, Issue 5, pp. 39-44, 2014.
[13] Sarah Swain, Shriya Mishra,” Prolego: A Data Science Approach to Predict the Outcome of a Football Match “, Vol. 6, Issue 4, pp. 132-136, 2018