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I have a file with 50000 rows from a library platform. Each individual row saves a user, and shows the order in which the user, has selected. The books could be from various categories (e.g. roman, history, etc..). There are a total of 10 categories. The categories that user has selected could be for example: 334664. This means this user has selected a book from categories 3, 4 and 6. How can I use this data to build a recommendation system using the k-means cluster algorithm. If anybody can help me how I can go through the whole process step by step.

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You can use a supervised Machine Learning approach, and unsupervised clusterization database gathering, your question can have multiple good answer. Do you have experience with building database compilation and dressing up calculable table to upload them and transfer them into matrix vector using for example Python 3.7 with Microsoft Azure Notebook.

There is many option you could consider but i would dress at least 3 different table to make a pre-trained model, conditionally your decision on chosen option then determinate categories and selected books, recommendation and user, from there you can start dressing up visual graphics, build your cells on your notebook or even do it first drawing on a paper to see the desired output. I could say many more things about computational calculation and how to train simultaneously multiple matrix vector, dimensional data but i need to make sure what you can do and where you are into this, if you understand what i am talking about. There might be other and easier way to do this...

I found this link here, it's almost the perfect explained quick tutorial i have seen since a while, you should have a look. I hope my answer did help you, regards.

kmeans clustering in python

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