Paper Details

The proposed approach in this project, presents a web based efficient application for creating an efficientuser friendly online sales predictor system. In this study, we have presented a model to analyse user history of ecustomers and extract information and make predictions about their shopping behavior on a digital market place. Wehave used a server-side program to collect clickstream data and users history , at the same time another java scriptprogram has been used to collect data from client side, It introduces new design for data mining system which combinesapp usage and content mining. Nowadays, An electronic market place has the advantage of offering more choices, lowerprices, easy search and access to online customers. That is why Internet market share is expanding every other day . Socustomers behavior patterns are also gaining importance in terms of buying or not buying. Application usage andclickstream data may reveal the behavior of users. This is also true for other software usage if it is connected to theinternet and a corporate server. So beside customers behavior, a detailed customer profile may also be extracted throughanalysis .

Prof. Shilpa Pimpalkar, Harshpriya Gaikwad, Shivani Parab, Snehali Patil, Pradnyashree Punase, “Sales Predictor: An online Product Sales Prediction Using User Online Behavior To Increase Overall Productivity”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 12, pp. -, December 2017.