Cost Snipper – Predicting Prices of Online Shopping Items based on Preceding Data
KR. Senthil Murugan1, S. Nagajothi2, C. Somasundaram3, R. Sabareesan4, K. Selvakumar5
1Mr. KR.Senthil Murugan , Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.
2Ms. S.Nagajothi, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.
3Mr. C. Somasundaram, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.
4Mr. R.Sabareesan, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.
5Mr. K.Selvakumar, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4405-4408 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9710038620/2020©BEIESP | DOI: 10.35940/ijrte.F9710.038620
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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: The variation of product prices in online shopping is high which makes it difficult to decide when to buy. The tremendous growth of e-commerce helps us to create the solution of price prediction. We used web scraping technique to get the price data from various online shopping retailers and process the data for each commodity to predict the price for the future which helps us to make decisions on buying online products. We automated the web scraping of data and price prediction daily to make the price available for the customer without any delay.
Keywords: Machine Learning, Web Scraping, Variation, Feature extraction, Prediction, Automation, SVM, Regression
Scope of the Article: Machine Learning.