A movie recommender system using item based collaborative filtering method in 1 file (project) and 4 algorithms in another file (project).
"Movie Recommender.ipynb" was my solution for TSEC Hackathon I attended during my engineering.
Dataset: https://grouplens.org/datasets/movielens/ ‘Recommended for education and development section’ full dataset of 265 MB.
Algorithms used: IMDb weighted average formula, KNN, Cosine Similarity (Content Based), Matrix Factorisation (Collaborative filtering).
GUI pending. Much more can be done in this. Will work on implementing neural networks, EDA, pre-processing, etc.
"ItemBasedCF.ipynb" was a part of an Udemy course I did.
Dataset: grouplens.org. "ml-100k"
A list of similar movies is recommended to a user based on his positive movie ratings and positive ratings of similar movies rated by other users.
Domain: Machine Learning;
Programming language: Python; IDE: Jupyter Notebook; Libraries: Pandas.