Скачать или смотреть Ashish Sahu- The Spark Foundation GRIP internship (Task 3): Implementing KMeans clustering algorithm

KMeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster.

In this video I have implemented KMeans model on student marks on the basis of class hours dataset.

Code : https://github.com/ashishsahu1/GRIP-S...

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