Clustering & Visualization of Clusters using PCA

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics.pairwise import cosine_similarity

import warnings
warnings.filterwarnings(action="ignore")

data= pd.read_csv("../input/CC GENERAL.csv")
print(data.shape)
data.head()

data.isnull().sum().sort_values(ascending=False).head()

data.loc[(data['MINIMUM_PAYMENTS'].isnull()==True),'MINIMUM_PAYMENTS']=data['MINIMUM_PAYMENTS'].mean()
data.loc[(data['CREDIT_LIMIT'].isnull()==True),'CREDIT_LIMIT']=data['CREDIT_LIMIT'].mean()

data.isnull().sum().sort_values(ascending=False).head()

columns=['BALANCE', 'PURCHASES', 'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'CREDIT_LIMIT',
        'PAYMENTS', 'MINIMUM_PAYMENTS']

for c in columns:
    
    Range=c+'_RANGE'
    data[Range]=0        
    data.loc[((data[c]>0)&(data[c]<=500)),Range]=1
    data.loc[((data[c]>500)&(data[c]<=1000)),Range]=2
    data.loc[((data[c]>1000)&(data[c]<=3000)),Range]=3
    data.loc[((data[c]>3000)&(data[c]<=5000)),Range]=4
    data.loc[((data[c]>5000)&(data[c]<=10000)),Range]=5
    data.loc[((data[c]>10000)),Range]=6

columns=['BALANCE_FREQUENCY', 'PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY', 
         'CASH_ADVANCE_FREQUENCY', 'PRC_FULL_PAYMENT']

for c in columns:
    
    Range=c+'_RANGE'
    data[Range]=0
    data.loc[((data[c]>0)&(data[c]<=0.1)),Range]=1
    data.loc[((data[c]>0.1)&(data[c]<=0.2)),Range]=2
    data.loc[((data[c]>0.2)&(data[c]<=0.3)),Range]=3
    data.loc[((data[c]>0.3)&(data[c]<=0.4)),Range]=4
    data.loc[((data[c]>0.4)&(data[c]<=0.5)),Range]=5
    data.loc[((data[c]>0.5)&(data[c]<=0.6)),Range]=6
    data.loc[((data[c]>0.6)&(data[c]<=0.7)),Range]=7
    data.loc[((data[c]>0.7)&(data[c]<=0.8)),Range]=8
    data.loc[((data[c]>0.8)&(data[c]<=0.9)),Range]=9
    data.loc[((data[c]>0.9)&(data[c]<=1.0)),Range]=10
    

columns=['PURCHASES_TRX', 'CASH_ADVANCE_TRX']  

for c in columns:
    
    Range=c+'_RANGE'
    data[Range]=0
    data.loc[((data[c]>0)&(data[c]<=5)),Range]=1
    data.loc[((data[c]>5)&(data[c]<=10)),Range]=2
    data.loc[((data[c]>10)&(data[c]<=15)),Range]=3
    data.loc[((data[c]>15)&(data[c]<=20)),Range]=4
    data.loc[((data[c]>20)&(data[c]<=30)),Range]=5
    data.loc[((data[c]>30)&(data[c]<=50)),Range]=6
    data.loc[((data[c]>50)&(data[c]<=100)),Range]=7
    data.loc[((data[c]>100)),Range]=8

data.drop(['CUST_ID', 'BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES',
       'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE',
       'PURCHASES_FREQUENCY',  'ONEOFF_PURCHASES_FREQUENCY',
       'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY',
       'CASH_ADVANCE_TRX', 'PURCHASES_TRX', 'CREDIT_LIMIT', 'PAYMENTS',
       'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT' ], axis=1, inplace=True)

X= np.asarray(data)

scale = StandardScaler()
X = scale.fit_transform(X)
X.shape

n_clusters=30
cost=[]
for i in range(1,n_clusters):
    kmean= KMeans(i)
    kmean.fit(X)
    cost.append(kmean.inertia_)  

plt.plot(cost, 'bx-')

kmean= KMeans(6)
kmean.fit(X)
labels=kmean.labels_

clusters=pd.concat([data, pd.DataFrame({'cluster':labels})], axis=1)
clusters.head()

clusters=pd.concat([data, pd.DataFrame({'cluster':labels})], axis=1)
clusters.head()

for c in clusters:
    grid= sns.FacetGrid(clusters, col='cluster')
    grid.map(plt.hist, c)

dist = 1 - cosine_similarity(X)

pca = PCA(2)
pca.fit(dist)
X_PCA = pca.transform(dist)
X_PCA.shape

x, y = X_PCA[:, 0], X_PCA[:, 1]

colors = {0: 'red',
          1: 'blue',
          2: 'green', 
          3: 'yellow', 
          4: 'orange',  
          5:'purple'}

names = {0: 'who make all type of purchases', 
         1: 'more people with due payments', 
         2: 'who purchases mostly in installments', 
         3: 'who take more cash in advance', 
         4: 'who make expensive purchases',
         5:'who don\'t spend much money'}
  
df = pd.DataFrame({'x': x, 'y':y, 'label':labels}) 
groups = df.groupby('label')

fig, ax = plt.subplots(figsize=(20, 13)) 

for name, group in groups:
    ax.plot(group.x, group.y, marker='o', linestyle='', ms=5,
            color=colors[name],label=names[name], mec='none')
    ax.set_aspect('auto')
    ax.tick_params(axis='x',which='both',bottom='off',top='off',labelbottom='off')
    ax.tick_params(axis= 'y',which='both',left='off',top='off',labelleft='off')
    
ax.legend()
ax.set_title("Customers Segmentation based on their Credit Card usage bhaviour.")
plt.show()