Movie-Recommender-System using ML

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  Movie Recommendation System

Movie-Recommender-System

Developed (Before GHW) a content-based movie recommender system that suggests movies to users based on their preferences. Utilized Python libraries such as Pandas for data processing and model implementation. Deployed the application using Streamlit for the web interface and Streamlit Cloud for hosting.

Concept used to build the model.pkl file : cosine_similarity

1 . Cosine Similarity is a metric that allows you to measure the similarity of the documents. 2 . In order to demonstrate cosine similarity function we need vectors. Here vectors are numpy array. 3 . Finally, Once we have vectors, We can call cosine_similarity() by passing both vectors. It will calculate the cosine similarity between these two. 4 . It will be a value between [0,1]. If it is 0 then both vectors are complete different. But in the place of that if it is 1, It will be completely similar.

Built With

• jupyter
• ml
• python
• streamlit