Clustering & Visualization of Clusters using PCA

Customer’s Segmentation based on their Credit Card usage behavior

Dataset for this notebook consists of the credit card usage behavior of customers with 18 behavioral features. Segmentation of customers can be used to define marketing strategies.

Content of this Kernel:

Data Preprocessing
Clustering using KMeans
Interpretation of Clusters
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()

(8950, 18)

Out[2]:

	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	TENURE
0	C10001	40.900749	0.818182	95.40	0.00	95.4	0.000000	0.166667	0.000000	0.083333	0.000000	0	2	1000.0	201.802084	139.509787	0.000000	12
1	C10002	3202.467416	0.909091	0.00	0.00	0.0	6442.945483	0.000000	0.000000	0.000000	0.250000	4	0	7000.0	4103.032597	1072.340217	0.222222	12
2	C10003	2495.148862	1.000000	773.17	773.17	0.0	0.000000	1.000000	1.000000	0.000000	0.000000	0	12	7500.0	622.066742	627.284787	0.000000	12
3	C10004	1666.670542	0.636364	1499.00	1499.00	0.0	205.788017	0.083333	0.083333	0.000000	0.083333	1	1	7500.0	0.000000	NaN	0.000000	12
4	C10005	817.714335	1.000000	16.00	16.00	0.0	0.000000	0.083333	0.083333	0.000000	0.000000	0	1	1200.0	678.334763	244.791237	0.000000	12

Data Preprocessing

Descriptive Statistics of Data

data.describe()

Out[3]:

	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	TENURE
count	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8949.000000	8950.000000	8637.000000	8950.000000	8950.000000
mean	1564.474828	0.877271	1003.204834	592.437371	411.067645	978.871112	0.490351	0.202458	0.364437	0.135144	3.248827	14.709832	4494.449450	1733.143852	864.206542	0.153715	11.517318
std	2081.531879	0.236904	2136.634782	1659.887917	904.338115	2097.163877	0.401371	0.298336	0.397448	0.200121	6.824647	24.857649	3638.815725	2895.063757	2372.446607	0.292499	1.338331
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	50.000000	0.000000	0.019163	0.000000	6.000000
25%	128.281915	0.888889	39.635000	0.000000	0.000000	0.000000	0.083333	0.000000	0.000000	0.000000	0.000000	1.000000	1600.000000	383.276166	169.123707	0.000000	12.000000
50%	873.385231	1.000000	361.280000	38.000000	89.000000	0.000000	0.500000	0.083333	0.166667	0.000000	0.000000	7.000000	3000.000000	856.901546	312.343947	0.000000	12.000000
75%	2054.140036	1.000000	1110.130000	577.405000	468.637500	1113.821139	0.916667	0.300000	0.750000	0.222222	4.000000	17.000000	6500.000000	1901.134317	825.485459	0.142857	12.000000
max	19043.138560	1.000000	49039.570000	40761.250000	22500.000000	47137.211760	1.000000	1.000000	1.000000	1.500000	123.000000	358.000000	30000.000000	50721.483360	76406.207520	1.000000	12.000000

Seems like data have many outliers!

Dealing with Missing Values

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

MINIMUM_PAYMENTS       313
CREDIT_LIMIT             1
TENURE                   0
PURCHASES_FREQUENCY      0
BALANCE                  0
dtype: int64

Imputing these missing values with mean

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()

TENURE               0
PRC_FULL_PAYMENT     0
BALANCE              0
BALANCE_FREQUENCY    0
PURCHASES            0
dtype: int64

Dealing with Outliers

By dropping outliers we can lose many rows as there are too many outliers in the dataset. So making ranges to deal with extreme values.

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)

Normalizing input values.

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

(8950, 17)

MODELING

Clustering using Kmeans

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-')

[<matplotlib.lines.Line2D at 0x7fcda94dca58>]

Choosing 6 no of clusters

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()

Out[15]:

	TENURE	BALANCE_RANGE	PURCHASES_RANGE	ONEOFF_PURCHASES_RANGE	INSTALLMENTS_PURCHASES_RANGE	CASH_ADVANCE_RANGE	CREDIT_LIMIT_RANGE	PAYMENTS_RANGE	MINIMUM_PAYMENTS_RANGE	BALANCE_FREQUENCY_RANGE	PURCHASES_FREQUENCY_RANGE	ONEOFF_PURCHASES_FREQUENCY_RANGE	PURCHASES_INSTALLMENTS_FREQUENCY_RANGE	CASH_ADVANCE_FREQUENCY_RANGE	PRC_FULL_PAYMENT_RANGE	PURCHASES_TRX_RANGE	CASH_ADVANCE_TRX_RANGE	cluster
0	12	1	1	0	1	0	2	1	1	9	2	0	1	0	0	1	0	5
1	12	4	0	0	0	5	5	4	3	10	0	0	0	3	3	0	1	1
2	12	3	2	2	0	0	5	2	2	10	10	10	0	0	0	3	0	3
3	12	3	3	3	0	1	5	0	2	7	1	1	0	1	0	1	1	5
4	12	2	1	1	0	0	3	2	1	10	1	1	0	0	0	1	0	5

Interpretation of Clusters

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

Cluster0 People with average to high credit limits who make all types of purchases
Cluster1 This group has more people with due payments who take advance cash more often
Cluster2 fewer money spenders with average to high credit limits who purchase mostly in installments
Cluster3 People with high credit limits who take more cash in advance
Cluster4 High spenders with high credit limits who make expensive purchases
Cluster5 People who don’t spend much money and who have average to the high credit limit

(Cluster number changes when re-run)

Visualization of Clusters

Using PCA to transform data into 2 dimensions for visualization

dist = 1 - cosine_similarity(X)

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

(8950, 2)

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()

And it’s done!

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Clustering & Visualization of Clusters using PCA

Customer’s Segmentation based on their Credit Card usage behavior

Data Preprocessing

Dealing with Missing Values

Dealing with Outliers

MODELING

Clustering using Kmeans

Interpretation of Clusters

Visualization of Clusters

Using PCA to transform data into 2 dimensions for visualization

Important Notice for college students

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Customer’s Segmentation based on their Credit Card usage behavior

Data Preprocessing

Dealing with Missing Values

Dealing with Outliers

MODELING

Clustering using Kmeans

Interpretation of Clusters

Visualization of Clusters

Using PCA to transform data into 2 dimensions for visualization

Important Notice for college students

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Like this:

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