Discover Movie & TV Show Trailers web app built with Angular 18 with TMDB API
Movie watching website with multiple servers . Integrates TMDB API . Streaming options available. For JIO users : bingflix2.vercel.app
Reel Rec - A Movie Recommendation AI designed to change the way movie enthusiasts discover and enjoy favorite films. Made using Django & Tailwind CSS.
Movie website with TMDB API
Ranjan'sFlix is a cutting-edge movie streaming platform designed to revolutionize the way you enjoy your favorite films. With a vast library of diverse genres, from classic cinema to the latest blockbusters, we offer a seamless and immersive viewing experience.
Pick 3 Movies, and Let Us Find Your Next Must-Watch!
Explore new movies , Rate them & add it to your List. You can also rate your favorite movie and add the movie to your own personalised list. The data of the list will also be stored in your browser so it is a complete app.
Reel Rec - A Movie Recommendation App designed to change the way movie enthusiasts discover and enjoy favorite films. Made using Django & Tailwind CSS.
A Movie/TV Show discovery app with support for external extensions
Movie trailer streaming app.
Frontend Website like IMDB using React.JS and Redux, TMDB
Tvflix is a simple and responsive web app built using Vanilla JS, leveraging the power of Postman and the TMDB API to seamlessly fetch and display comprehensive movie details. This project serves as a template for larger applications.
Use-Popcorn is a React.js web application that allows users to search for movies and view detailed information about each movie using the OMDb API. Users can also add movies to a personalized watchlist, making it easy to keep track of films they want to watch.
Submision Dicoding Indonesia - Machine Learning Terapan (Movie Recomendation)
This is a movie night tracking and voting app
Movie Recommendation System A TF-IDF based movie recommendation system that suggests movies based on your preferences. With a database of 10,000 movies, this system is designed to enhance your movie-watching experience by recommending films similar to the ones you've enjoyed.
An ML-based movie recommendation system built using a dataset from Kaggle. This project preprocesses movie data to generate recommendations based on cosine similarity. The system uses Python libraries such as Pandas, NumPy, NLTK, and sklearn for data processing and machine learning. The user interface is developed with Streamlit.
App for discovering and building your personal movie library, with films as numerous as stars in the night sky, all gathered in one place.
End to End Machine Learning Project using Content Based Filtering
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