Agrupamento_Hierárquico_Base_Crédito.py

import  plotly.express as px
import plotly.graph_objects as go
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

import pandas as pd

base_credit= pd.read_csv(r'C:\Users\joaov\OneDrive\Área de Trabalho\python\CSV\credit_card_clients.csv', header=1)

print(base_credit)

#DIVIDA TOTAL
base_credit['bill_total']=base_credit['BILL_AMT1'] + base_credit['BILL_AMT2'] + base_credit['BILL_AMT3'] + base_credit['BILL_AMT4'] + base_credit['BILL_AMT5'] + base_credit['BILL_AMT6']

print(base_credit)
#limite e bill total
x_cartao= base_credit.iloc[:,[1, 25]].values

#padronizando
scaler=StandardScaler()
x_cartao=scaler.fit_transform(x_cartao)
print(x_cartao)

import matplotlib.pyplot as plotly
from scipy.cluster.hierarchy import dendrogram, linkage

dendograma= dendrogram(linkage(x_cartao, method='ward'))
plotly.show()

from sklearn.cluster import AgglomerativeClustering

hc_salario=AgglomerativeClustering(n_clusters=3, linkage='ward', affinity='euclidean')
rotulos=hc_salario.fit_predict(x_cartao)
print(rotulos)


grafico = px.scatter(x = x_cartao[:,0], y = x_cartao[:,1], color = rotulos)
grafico.show()