A content-based movie recommendation system using specific details such as the crew, cast, and genre to submit similarity recommendations to the user.
**Domain : Data Analytics.
Sub-Domain : Recommender System. Techniques : Content-based Filtering.**
Description
1. Developed user-based movie recommendation system by implementing content-based filtering based on the cast, crew and genre.
2. Used tmdb movie dataset containing 4803 records for developing the recommendation system.
3. Implementation in Python colaboratory notebook.
4. Flask server Implementation for basic web template display
For Python implementation:
1. Please open "Recommendation_System.ipynb2" file in python notebook
2. Provide "tmdb_credits.csv" and "tmdb_movies,csv" file location in each file read function.
3. Provide "movies.html" file location for web template rendering (remember to edit the template folder location as appropriate.)
3. Run all cells in the file.
4. Go to "https://azf7djmtvzs-496ff2e9c6d22116-5000-colab.googleusercontent.com/"
5. Enter inputs
6. Get recommendations (or run the chill function in the python notebook)
****Languages : Python,HTML5****
**Tools/IDE : Google Colab, Sublime, Flask**