Market-basket analysis - Movie recomendation
Budget: €50 – €51 EUR
The task is to implement an algorithm finding frequent itemsets (aka market-basket analysis), analyzing IMDB datasets described below
The IMDB dataset is published on Kaggle, under IMDb non-commercial licensing. The analysis must be done considering movies as baskets and actors as items.
https://www.kaggle.com/ashirwadsangwan/imdb-dataset
The dataset should not be added to the repository, but downloaded during code execution, for instance via the kaggle API (https://github.com/Kaggle/kaggle-api). Code should be implemented using a jupyter notebook executable on Google colab, possibly adding a badge/link directly from the repository to the colab version of the notebook.
Code in colab notebook with comments
short report written in LaTeX regarding the algorithms implemented
The IMDB dataset is published on Kaggle, under IMDb non-commercial licensing. The analysis must be done considering movies as baskets and actors as items.
https://www.kaggle.com/ashirwadsangwan/imdb-dataset
The dataset should not be added to the repository, but downloaded during code execution, for instance via the kaggle API (https://github.com/Kaggle/kaggle-api). Code should be implemented using a jupyter notebook executable on Google colab, possibly adding a badge/link directly from the repository to the colab version of the notebook.
Code in colab notebook with comments
short report written in LaTeX regarding the algorithms implemented