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What's K Mean Clustering ?

it's an unsupervised learning algorithim where we use features to group data rows into clusters, we will discover possible labels for clusters without having y hat to compare to .

Diffrent methods can be used to decide the number of clusters , but we will use the elbow method .

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K Mean Clustering Rules

1- Each point must belong to a cluster .

2- Each point must belong to a single cluster .

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K Mean Clustering Steps

1- Choose K value

2- Randomly select k distincit data points

3- we will treat the selected points as cluster points in which data point closest to would be classified as them .

4- Calculate mean vector value for each cluster & assign it as the cluster center .

5- iterate to all data points , what cluster cenetrs these points
are closest to ? some points may be reassigned .

6- repeat from step 4

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Elbow Method

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