Automatic prediction of whether a customer will come into the shop or not So as my university project I am planning to make a prediction system as described in the title. My current idea is to use the age/gender classifier and run it on a video(taken in front of a shop) which outputs a csv file of the age/gender/Customer ID. In addition, I will use the existing data of the shop of who came in/who didn't come into the shop but passed by the shop and by running XGBoost on this csv data I can predict which customer will come into the shop or not. 

Do you think this idea is possible? Is there any other way to implement this idea. It would also be great if we could implement this in such a way as to make the deep learning model learn the various features of those who come into the shop or not.
This should be possible, but it's not completely clear what you are trying to do.

If you're trying to predict customer age and gender from a video, then you've got a computer vision problem. Deep learning methods are the state of the art for this, and probably some sort of convolutional deep neural network is your friend. 

If you're trying to predict whether the customer will enter the shop from their age and gender, then @DuttA suggestion to start with simple classification techniques is probably the best bet. Try out logistic regression as a starting point. 

If you really want to do both of these things at once, you can again try out a deep convolutional neural network. 

All that said, it's not completely clear that there is signal in the videos you want to collect: it might be that predicting whether they'll enter the shop is not really possible on the basis of a short video. The only way to know for sure is to give it a try however.