movie_recommendation

Repository for the movie_recommendation project in OC Data Scientist path

Main objective : Create a movie recommendation tool using unsupervised exploration

This project has been tackled in two separate phases :

Phase 1) Early project in the DS path, with a 5000 films database (IMDb) we aim to develop a recommender tool using unsupervised learning from the dataset (nearest neighbors algorithm)
Phase 2) Last project of the DS path, with 53000 films of this database (IMDb) and only using genres and keywords, we train a neural network with two embeddings layers using Will Koehrsen's work (https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526) on book recommendation for our own movie's recommendation purpose. Results are better than I was expected and the recommender, through the powerful non-linear behaviour learning of the neural network, is outputting some accurate-enough recommendations

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
data		data
README.md		README.md
movie_recommendation_data_processing.ipynb		movie_recommendation_data_processing.ipynb
movie_recommendation_unsupervised_exploration.ipynb		movie_recommendation_unsupervised_exploration.ipynb
movies_recommender_embedding.py		movies_recommender_embedding.py
movies_recommender_nearest_neighbours.py		movies_recommender_nearest_neighbours.py
words_embedding-casting.ipynb		words_embedding-casting.ipynb
words_embedding-genres.ipynb		words_embedding-genres.ipynb
words_embedding-new_modelling-emb_size_100.ipynb		words_embedding-new_modelling-emb_size_100.ipynb

Provide feedback