Content-Based Movie Recommendation System
MovieMind is a machine-learning powered movie recommendation web application that suggests movies similar to a user’s selection. It uses content-based filtering with cosine similarity, displays real-time movie posters, and is deployed on Microsoft Azure App Service.
1.Select a movie from a dropdown
2.Get Top-5 recommended movies
3.View posters fetched live from TMDB
4.Fully cloud-deployed with CI/CD
5.Handles large ML model files dynamically
🎯 Content-based movie recommendations
🧠 Cosine similarity on pre-computed feature vectors
🖼️ Real-time poster & metadata fetching via TMDB API
☁️ Cloud-friendly handling of large .pkl files
🌐 Live deployment on Azure App Service (Linux)
1.User selects a movie
2.System finds its index in the dataset
3.Cosine similarity scores are computed
4.Top-5 similar movies are selected
5.Posters are fetched from TMDB API
6.Results are rendered using Streamlit
1.Programming & ML
2.Python 3.11
3.Pandas, NumPy
4.Scikit-learn
5.Web & UI
6.Streamlit
7.Cloud & DevOps
8.Azure App Service (Linux)
9.GitHub Actions (CI/CD)
TMDB (The Movie Database) API
Google Drive (large model storage)
Krish Mishra B.Tech – Information Technology Interested in Machine Learning, Cloud Deployment, and Backend Systems
🔗 GitHub: https://github.com/krishmishra09
🔗 Application URL: //https://moviemind.azurewebsites.net/