Kmeans clustering in python - Giving original labels to predicted clusters

Question

I have a dataset with 7 labels in the target variable.

X = data.drop('target', axis=1)
Y = data['target']
Y.unique()

array(['Normal_Weight', 'Overweight_Level_I', 'Overweight_Level_II',
       'Obesity_Type_I', 'Insufficient_Weight', 'Obesity_Type_II',
       'Obesity_Type_III'], dtype=object)

km = KMeans(n_clusters=7, init="k-means++", random_state=300)
km.fit_predict(X)
np.unique(km.labels_)

array([0, 1, 2, 3, 4, 5, 6])

 

After performing KMean clustering algorithm with number of clusters as 7, the resulted clusters are labeled as 0,1,2,3,4,5,6. But how to know which real label matches with the predicted label.

In other words, I want to know how to give original label names to new predicted labels, so that they can be compared like how many values are clustered correctly (Accuracy).