skekre98 / NBA-Search

flask application designed to explore NBA statistics :basketball:
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Implement optimal agglomerative clustering #96

Open skekre98 opened 4 years ago

skekre98 commented 4 years ago

Description of feature/enhancement This function should create the optimal clusters via agglomerative clustering in the ClusterStore.

Description of implementation https://github.com/skekre98/NBA-Search/blob/0564a752c77a7c420890c44e6873c53111251fe5/inference/cluster_store.py#L78 There is likely an elegant way of doing this with sklearn without having to find the optimal number of clusters manually. See build_optimal_kmeans_clusters for how this was done for KMeans. This function should follow a Heirarchical Approach and start of treating each object as a singleton cluster.

Why this is a good feature/enhancement Would be nice to see how different clustering algorithms perform on different sets of data.

Additional context n/a

RaihanSabique commented 4 years ago

@skekre98 i can do this. Need some times (approximate 1 week). Currently I'm little bit busy with some other staff.

skekre98 commented 4 years ago

@RaihanSabique no worries, take you time ☺️. Thanks for seeing the entire component through!

kelaaron commented 3 years ago

Hi @skekre98, is this issue available? Would love to work on it but might need a couple weeks to get familiar with agglomerative clustering. @RaihanSabique, are you still trying to implement this?

skekre98 commented 3 years ago

@kelaaron Awesome, this issue may require more than one person. Thanks!

RaihanSabique commented 3 years ago

Sorry. I was little busy with my graduate admission. Hope now i can help @skekre98

skekre98 commented 3 years ago

Sorry. I was little busy with my graduate admission. Hope now i can help @skekre98

Hi @RaihanSabique, please go ahead! I think @kelaaron is also interested in working on this if you'd like to guide him on what you've already done with the other functions.

DevChoganwala commented 2 years ago

Hi @skekre98, are you still looking for help on this? I could help!