K-Means Clustering of Numerical Data with sklearn in Python Template
The K-Means Clustering of Numerical Data with sklearn in Python template shows how to solve a simple clustering problem using the K-Means algorithm provided by the sklearn machine learning package. After performing the clustering, we will visualize the results and identify the clusters. Some other related topics you might be interested in are The Elbow Method for K-Means Clustering in Python, Heatmaps and Dendrograms with seaborn in Python, K-Means Clustering of Categorical Data with sklearn in Python. You can now download the Python template for free. The K-Means Clustering of Numerical Data with sklearn in Python is among the topics covered in detail in the 365 Data Science program.
Who is it for
This is an open-access Python template that is going to be very helpful for Data Analysts, Data Scientists, Machine Learning Engineers and anyone who wants to familiarize themselves with k-means clustering – a type of unsupervised machine learning algorithm.
How it can help you
Clustering is a type of unsupervised machine learning algorithm. What this means is that the data points in a dataset are not accompanied by a target value. Instead, once clusters have been constructed, it is the user who has to interpret the results. This template can be used whenever the dataset consists of numerical features and the samples need to be divided into yet unknown categories.