Overview / Usage

The model is currently working on 5000 movies. Its is successful in showing user the recommendation of the movies based on his taste in Genre and Movie Type. The model is also capable of fetching the imdb ratings and movie poster and last but not least it is also capable to land user directly to the imdb webpage of that movie.

Methodology / Approach

The model is based on KNN (K- Nearest Neighbor) and Euclidean Distance.

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Technologies Used

The model has been made by using:-

Python, Numpy, Json, Streamlit

Repository

https://github.com/kunal9718/Movie-Recommender