Recommendations allow apps to use machine learning to intelligently serve the most relevant content for each user. They take into account past user behavior to suggest app's content the user might like to interact with in the future by using a model trained on the aggregate behavior of a large number of other users.

This tutorial shows how to obtain data from your app's users with Firebase Analytics, build a machine learning model for recommendations from that data, and then use that model in an Android app to run inference and obtain recommendations. In particular, our recommendations will suggest which movies a user would most likely watch given the list of movies the user has liked previously.

more detils:

• user login
• user register
• user profile
• user detils
• admin login
• admin histry

With project:- Report, PPT, Class Diagram, Setup Your Device, All Doubts are Clear, Viva Questions
Software Requirements:

Operating System: Windows10,11,12.
APPLICATION/SOFTWARE - ANDROID STUDIO, CODE EDITTER.
Application Server:XAMPP,FIREBASE DATABASE ,SQL ETC.
Frontend designing languages: XML,MATERIAL DESIGN, LIBRARY USE,DESIGN THROW APIAND LIBRARY.
Backend :- JAVA,KOTLIN,FLUTTER.
Database:  XAMPP,FIREBASE DATABASE ,SQL ETC.
Database Connectivity:   PhpMyAdmin, FIREBASE, ADMIN SIDE.

Demo Video or Any query Contact:- 9306637630(Whatsapp/Call)
if you by project form project bhandar. project bhandar team share source code, ppt, report,and run project your system throught online or connect your system throught anydesk/teamviwer.
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