Developed an Online Movie Recommender app using content based and collaborative filtering algorithms based on Machine Leaning. A small subset of the popular MovieLens 25M dataset was used and the app was built using Django, a high-level web framework based on Python.
What new feature can help drive the engagement of Netflix among moderate users.
User Persona: Moderate watchers who watch less than 2 hrs per day and have completed less than 10 shows.
Deciding what to watch
Lack of Theater like experience
Recommender systems could be improved where region wise/state wise recommendations are displayed to the user. Also peer groups can be formed where every user can see the preferences, ratings and reviews of shows watched by other users
Watch Parties can be introduced where a group of friends or colleagues watch a show together with integrated Discord like feature where chat with each other.
Creative Shock Case Study Competition 2019: Global Rank 20
StartUp Manager, ShARE: ShARE is an innovative start-up at the crossroads of education and consulting. It runs a leadership programme advocating Do Well Do Good leadership, and helps the corporates serve both the shareholders and the society.
Sub-Editor, Awaaz, IIT Kharagpur: Co-lead a team of students that ensure proper functioning of Awaaz as media body of IIT Kharagpur. Also serve as the Page Editor of it’s facebook handle which is followed by more than 33k users.