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when u watch a trailer on our website
Inspiration
we wanted to solve the problem of not getting the exact content we want when we want to binge watch
What it does
Movie Trix is a movie recommendation system that offers generalized recommnendations to every user based on movie popularity, genre, and year. The model also give personalized recommendations based on the user's choice of genre and cast. Finally, the system suggests similar movies have a higher probability of being liked based on the movie selected by user.
How we built it
Tech Stack and Software requirements: Frontend: HTML5, CSS3, JavaScript, BootStrap, jQuery Backend: Python flask ML model: Jupyter Notebook IDE: PyCharm Version Control: Git Based on chosen genres and cast Popular movies based on the genre Popular movies based on the year Similar movie recommendation
Challenges we ran into
the main challenge was to train the model using the vast dataset which we had to clean and sort and merge. database connectivity was itself a big challenge. privacy, duplication etc.
Accomplishments that we're proud of
the biggest accomplishment is the successfull completion of the project using some new technologies which was a thrilling experience.
What we learned
we learned new ML technologies, and most of all the stress and time management when u have limited time in which we also had to schedule our sleep as well and working with new techs, learning them while working on the project.
What's next for Movie Recommender system
there are like and dislike button which store data which can be used for further recommendation of the movies and collaborative filtering can be used by using the data of the users around the user's location and the one's who watch movies with same genres, cast, crew etc.
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