First K-Means Test with Scikit-Learn

Dask for Machine Learning¶. This is a high-level overview demonstrating some the components of Dask-ML. Visit the main Dask-ML documentation, see the dask tutorial notebook 08, or explore some of the other machine-learning examples. Based on the validation test results, the model can be trained(for instance, changing parameters, classifiers). This will help us get the most optimized model. ... K-means Clustering Algorithm For ... K-means clustering is used in all kinds of situations and it's crazy simple. The R code is on the StatQuest GitHub: https://github.com/StatQuest/k_means_clus... Returns statistic float or array. s^2 + k^2, where s is the z-score returned by skewtest and k is the z-score returned by kurtosistest.. pvalue float or array. A 2-sided chi squared probability for the hypothesis test. References. 1. D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size”, Biometrika, 58, 341-348 The K-Means algorithm needs no introduction. It is simple and perhaps the most commonly used algorithm for clustering. The basic idea behind k-means consists of defining k clusters such that total… K-means clustering is a technique in which we place each observation in a dataset into one of K clusters. The end goal is to have K clusters in which the observations within each cluster are quite similar to each other while the observations in different clusters are quite different from each other.

2022.01.27 13:42 Agitated_Ostrich_339 First K-Means Test with Scikit-Learn

I'm working on a new project (K-means Clustering) for a client and wondered if anyone had some insights on things I should keep an eye out for to ensure that I produce correct results?
Background: Over the last six-eight months, I have spent a lot of time working in and learning Pandas & SQL. Panadas is an excellent replacement for repeatable tasks within Excel. However, this is my first big ML project / Data Science project, and I'm feeling a bit overwhelmed. It's been years since I studied statistics, probability, and calculus. Any advice or resources would be greatly appreciated!
Below are some of the resources that I have been reading to prep for the project:
https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
https://towardsdatascience.com/customer-segmentation-in-python-9c15acf6f945
https://developers.google.com/machine-learning/crash-course
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