Movie Recommender System

🎬 Project: Movie Recommender System

👨‍💻 Author: Shailesh Gupta

🔗 GitHub: View Source Code

🧠 What is this project about?

This is a content-based movie recommendation system that suggests similar movies based on your selected title. It uses movie metadata (genres, tags, cast, crew) to compute similarity scores and recommend relevant titles.

The model uses TF-IDF vectorization and cosine similarity to find the top 5 similar movies from a large movie dataset.

📦 Key Features:

  • Combines features like overview, cast, genres, and keywords into a single vector
  • Similarity computed using cosine_similarity from scikit-learn
  • Interactive Streamlit frontend with easy-to-use dropdown for movie selection
  • Top 5 movie recommendations displayed with titles and (where available) posters

⚠️ Deployment Note:

This app could not be hosted on Streamlit Cloud due to GitHub file size limitations. Some files like tmdb_5000_movies.csv and pickled models are large and exceed GitHub’s limit.

But you can still try it locally by downloading the necessary files and running it from your own system. All assets are provided below:

📁 Google Drive (Download Required Files):
👉 Click Here to Access the Data & Models

🚀 How to Run Locally:

  1. 📥 Clone the repository:
    git clone https://github.com/sg2499/Movie-Recommender-System.git
  2. 📂 Download and place the following files in appropriate folders:
    - tmdb_5000_movies.csv
    - pickle/movie_list.pkl
    - pickle/similarity.pkl
  3. ▶️ Run the app:
    streamlit run app.py

You'll be able to interact with the recommendation engine via your browser instantly!

📷 App Preview:



🖼️ Note on Poster Images:

The app attempts to fetch movie posters using an external API. However, not all movie titles return valid image URLs. So while recommendations always work, some posters may be missing in the output. This is expected behavior.

💡 Why This Project is Valuable:

  • Gives hands-on experience with TF-IDF + cosine similarity based recommender systems
  • Applies NLP and vectorization techniques on real-world entertainment data
  • Perfect for learning how platforms like Netflix and Prime Video use metadata for personalization

🙌 Let’s Connect

If you liked this project, feel free to star the GitHub repo ⭐ or connect with me on LinkedIn.

More recommendation and AI-powered projects coming soon!

🔖 Tags:
Movie Recommendation, TF-IDF, Cosine Similarity, NLP, Pickle Models, Streamlit, Recommender Systems, Python, Machine Learning

Location: India

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