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
    #km = MiniBatchKMeans(n_clusters=k, batch_size=100, verbose=1).fit(dataset_to_predict) #Also tried on part of the dataset
    sse.append(km.inertia_)

y_predicted = km.fit_predict(dataset_to_predict) #this line throw error
y_predicted