Market-basket analysis - Movie recomendation - Algorithm development + report
Budget: ₹7,500 – ₹7,501 INR
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.
Try at least 2 algorithms and do a comparison on the result
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.
Try at least 2 algorithms and do a comparison on the result
Code in colab notebook with comments
short report written in LaTeX regarding the algorithms implemented