TensorFlow Based Website Click through Rate (CTR) Prediction Using Heat maps

Web Heat Maps are used to identify the click patterns and activities visited by the users of the website. Using heat maps, one can make a manual decision based on the user's click activity. This paper proposes a framework using TensorFlow to identify and detect the users click activity in real time. Tensor Flow also suggest or take business decisions predicted through users clicks. This paper models Tensor Flow's machine learning library to take automated decisions like placement of suitable products, placement of advertisements and others based on the highest clicks recorded by the users. The results predict that the future businesses like e-commerce, fashion and retail industry can benefit more if this framework is deployed in such applications.