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CLASSIFICATION OF E-COMMERCE DATA USING DATA MINING

This paper discuss the development of an application for both consumers and companies, which would make use of information available on E-commerce websites to classify the data in accordance with the quality of the products available.Electronic commerce or E-commerce refers to the buying and selling of products over electronic systems such as the Internet. The amount of trade conducted electronically has grown extraordinarily with widespread Internet usage. In today’s world the major players such as Amazon, Flipkart and eBay have a large database of products and a number of consumers that use these services. Since, there is such a large amount of data available with no one to collate and understand it,this paper attempts to classify the data to benefit both consumers and companies. The classification is done using two popular algorithms – Naïve Bayes and Decision Tree which are supervised learning methods. A comparison of the classification is done to understand which algorithm produces better results.The result which we have obtained using the mobile phone datasets shows that Decision Tree performs better than Naive Bayes.Once such an application is built competitors can understand how their competitors are priced and consumers understand what quality of products are available. This is mutually beneficial making the E-commerce world a flatter playing ground.