Partitioning a dataset with an optimum number of clusters

In this video, I'm showing how to determine the optimum number of clusters with K-Means Machine Learning. Specifically, I discuss the inertia measurement and the elbow method. Finally, I use a dataset and compute the optimum number of cluster and then run K-mean clustering in Python. Through this video (project), I'm using several libraries such as Pandas, Numpy, Matplotlib, Seaborn and SKlearn.
You can find the PDF version of these codes via this URL:
drive.google.c...
You can also access the entire code in Jupyter notebook via my Github account :
github.com/nae...

[@AnalyzeIt_Nael]

You can find the PDF version of these codes via this URL
drive.google.com/file/d/1Ghna1RtQcVzdCBHxLtKur66xjukMnd5M/view?usp=share_link
You can also access the entire codes in Jupyter notebook via my Github account :
github.com/naeljb/python/blob/main/KMeans_elbow_project.ipynb

[@qasimali-gu3oz]

Please also sentiment analysis in power query "dataset call" feature through python package like NLTK.

[@AnalyzeIt_Nael]

Thank for your suggestion, this is an area that I'm currently investigating as well and how this can work best with Power BI.