customer personality.py

scaler=StandardScaler()
dataset_temp=data[['Income','Seniority','Spending']]
X_std=scaler.fit_transform(dataset_temp)
X = normalize(X_std,norm='l2')

gmm=GaussianMixture(n_components=4, covariance_type='spherical',max_iter=2000, random_state=5).fit(X)
labels = gmm.predict(X)
dataset_temp['Cluster'] = labels
dataset_temp=dataset_temp.replace({0:'Stars',1:'Need attention',2:'High potential',3:'Leaky bucket'})
data = data.merge(dataset_temp.Cluster, left_index=True, right_index=True)

pd.options.display.float_format = "{:.0f}".format
summary=data[['Income','Spending','Seniority','Cluster']]
summary.set_index("Cluster", inplace = True)
summary=summary.groupby('Cluster').describe().transpose()
summary.head()