Abstract:
Large-scale online commerce is growing. Internet shoppers buy items. Simply select and pay for their stuff. Products arrive to customers’ doors. Online shopping simplified and accelerated life. Online buying is growing, therefore more data about people’s online actions is available. Many applications can profit from such data.
Web data can reveal user behavior and online customer classification. We proposed an online shopping behavior extraction technique. The system will graph user internet activities. This graph aids admin decision-making. To simulate decision making, we offer a graphical hidden state model based on statistical features and incorporate all accessible information.
The proposed technique improved million click datasets by 30%. This web application will display various items. User can browse and buy things. System tracks sequential user behavior and displays it graphically to aid decision-making.
This system tells the admin which products customers buy most. Administrators will also learn which products are popular. So he can base his decision on customer online activity. As user behavior pattern is displayed graphically, admins can better evaluate the data and make decisions and solutions faster.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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