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- from sklearn.cluster import KMeans, MiniBatchKMeans
- km = KMeans(n_clusters=5)
- k_range = range(1,10)
- sse = []
- for k in k_range:
- km = KMeans(n_clusters=k).fit(dataset_to_predict) #this line throw error
- sse.append(km.inertia_)
- y_predicted = km.fit_predict(dataset_to_predict) #this line throw error
- y_predicted
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