df = seaborn.load_dataset("iris")
df_features = df.drop(labels=["species"], axis=1)
df_target = df.filter(items=["species"])

Please note that a machine learning model only understands numbers. But the target labels are strings. So, we will need to label encode the target column of the dataset.

encoder = LabelEncoder()
df_target["species"] = encoder.fit_transform(df_target["species"])

Now, we are using the k-means clustering algorithm to cluster the data points. Please note that we are dividing the data points into three clusters. And the random_state=1 parameter controls the random number generator that is used to initialize the k centroids of the cluster (How does k-means clustering work?)

The fit_predict() method here learns from the dataset, clusters the data points, and then labels all the clusters to which the data points belong.

kmeans = KMeans(n_clusters=3, random_state=1)
y_pred = kmeans.fit_predict(df_features)

Now, we can compare y_pred with the actual labels of the target and measure the completeness score.

score = completeness_score(df_target["species"], y_pred)

The output of the above program will be:

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa

Completeness Score:  0.7649861514489816