K Means clustering algorithm is unsupervised machine learning technique used to cluster data points. In this tutorial we will go over some theory behind how k means works and then solve income group clustering problem using sklearn, kmeans and python. Elbow method is a technique used to determine optimal number of k, we will review that method as well.
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Code: https://github.com/codebasics/py/blob/master/ML/13_kmeans/13_kmeans_tutorial.ipynb
data link: https://github.com/codebasics/py/tree/master/ML/13_kmeans
Exercise solution: https://github.com/codebasics/py/blob/master/ML/13_kmeans/Exercise/13_kmeans_exercise.ipynb
Topics that are covered in this Video:
0:00 introduction
0:08 Theory - Explanation of Supervised vs Unsupervised learning and how kmeans clustering works. kmeans is unsupervised learning
5:00 Elbow method
7:33 Coding (start) (Cluster people income based on age)
9:38 sklearn.cluster KMeans model creation and training
14:56 Use MinMaxScaler from sklearn
24:07 Exercise (Cluster iris flowers using their petal width and length)
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