FIND INCREDIBLE MOVIES TO WATCH 🎬✌️
Have you ever wondered how Netflix manages to recommend so many random movies that all somehow fit your preferences in one way or another? Most, if not all, of it has to do with data science, but more specifically, big data, machine learning, and artificial intelligence.Taking Netflix's Recommendation we'have also planned build our own.In this Project, we are going to Build a Movie Recommendation a hybrid Movie Recommendation Engine as there various kind of Recommendation Like Content and Collabrative filtering,The dataset we are going to we have used is the Movie dataset Online available which have Almost 4800+ Movies and movies upto 2017 and and also have collected remaining data of years 2018-2020 from wikipidia,We have also used The Movie database(TMDB) API for other importent details.
-If you want to view the deployed model, click on the FOLLOWING LINK
BUCKITO💜
Beautiful isn't it.
-If you are searching for Code, Algorithms, Which similarity metrics I used and much more Please Open "FinalModel.ipynb" file inside Notebook Folder.
THE TMDB DATASET📋
WIKIPIDIA-2018 WIKIPIDIA-2019
Application Programming Interface📘
Popularity based recommendation engine:📶 Perhaps, this is the simplest kind of recommendation engine that you will come across. The trending list you see in YouTube or Netflix is based on this algorithm. It keeps a track of view counts for each movie/video and then lists movies based on views in descending order(highest view count to lowest view count). Pretty simple but, effective. Right?
Content based recommendation engine:
Collaborative filtering based recommendation engine:👬 This algorithm at first tries to find similar users based on their activities and preferences (for example, both the users watch same type of movies or movies directed by the same director). Now, between these users(say, A and B) if user A has seen a movie that user B has not seen yet, then that movie gets recommended to user B and vice-versa. In other words, the recommendations get filtered based on the collaboration between similar user’s preferences (thus, the name “Collaborative Filtering”). One typical application of this algorithm can be seen in the Amazon e-commerce platform, where you get to see the “Customers who viewed this item also viewed” and “Customers who bought this item also bought” list.
REACT REPOSITORY 🌠
This Repository Contains Fronted and Backend Implementation of this Application. :triangular_flag_on_post:
Upgrade pip before install fastapi python -m venv fastapi
Upgrade pip before install fastapi python -m pip install --upgrade pip
To run the server uvicorn server:app --reload
To run the FASTAPI POSTMAN http://127.0.0.1:8000/docs