elizabeth-mathew1/movierecommender

A basic movie recommendation system, which uses content-based filtering to suggest the top 5 similar movies as per the user's search.

🎭 MovieRecommender

A simple web application that suggests you movies based on your entry.

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🎓 About the project

Movie Recommender recommends you top five movies based on a single entry of a movie. The project explores content based filtering of data, which is a type of recommender system that attempts to guess what a user may like based on that user’s activity. Content-based filtering makes recommendations by using keywords and attributes assigned to objects in a database. Youtube uses CBF along with other filtering techniques. The backend service is provided by streamlit and is hosted on Heroku.

The different stages of the project included:

1. Preprocessing where the data from two csv files which contained information about 4086 movies were processed for futher detailing.
2. Model building where a model was trained using the algorithm of k-nearest neighbours using CountVectorizer and cosine_similarity libraries.
3. Website building done using streamlit
4. Deployed on Heroku

Click here to view the deployment

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✏️ Tools Used

1. Python 2.7 or greater
2. Natural Language Toolkit (NLTK)
3. Streamlit
4. Pickle
5. Requests
6. Sci-Kit learn
7. Numpy

and a few other libraries and dependencies for preprocessing.

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🎪 Screenshots

1. Opening Screen

2. Option Menu - I

3. Recommendations - I

4. Recommendations - II

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📍 Click Here to test the application on your own.

🌟 If you liked the application, make sure to star this repo, Thankyou.