kmeans = KMeans(n_clusters=4)
    
    model = kmeans.fit(europe_july)
    pred = model.labels_
    europe_july['cluster'] = pred
    
    pca = PCA(n_components=2)
    pca_model = pca.fit_transform(europe_july)
    data_transform = pd.DataFrame(data = pca_model, columns = ['PCA1', 'PCA2'])
    data_transform['Cluster'] = pred
    
    plt.figure(figsize=(8,8))
    g = sns.scatterplot(data=data_transform, x='PCA1', y='PCA2',\
                        palette=sns.color_palette()[:4], hue='Cluster')
    title = plt.title('World countries clusters with PCA')


[![PCA][1]][1]

But when I run this code it does not seem to take into account this model.

    europe_july['country'] = countries
    europe_july['iso_alpha'] = iso_alpha
    
    
    fig = px.choropleth(data_frame = europe_july,
                        locations= "iso_alpha",
                        scope= 'world',
                        title='2020-11-07 (World)',
                        color= "cluster",
                        hover_name= "country",
                        color_continuous_scale= 'earth',
                        )
    
    fig.show()


Since this is the output that I get, as you can see there is clearly a cluster with only one country, when there is no such cluster predicted by the model.

[![vis][2]][2]

Could someone please guide on why my visualisation is wrong?


  [1]: https://i.sstatic.net/8ljXn.png
  [2]: https://i.sstatic.net/Tp7fR.png