In this post, we do a segmentation analysis for the Customer data set
Importing required libraries:
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
import plotly as py
import plotly.graph_objs as go
from sklearn.cluster import KMeans
import warnings
import os
warnings.filterwarnings("ignore")
py.offline.init_notebook_mode(connected = True)
#print(os.listdir("../input"))Loading the Costumer data set:
df = pd.read_csv(r'Mall_Customers.csv')Visualization:
plt.style.use('fivethirtyeight')
plt.figure(1 , figsize = (15 , 6))
n = 0
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
n += 1
plt.subplot(1 , 3 , n)
plt.subplots_adjust(hspace =0.5 , wspace = 0.5)
sns.distplot(df[x] , bins = 20)
plt.title('Distplot of {}'.format(x))
plt.show()
plt.figure(1 , figsize = (15 , 5))
sns.countplot(y = 'Gender' , data = df)
plt.show()
Relationship between data features
plt.figure(1 , figsize = (15 , 7))
n = 0
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
for y in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
n += 1
plt.subplot(3 , 3 , n)
plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
sns.regplot(x = x , y = y , data = df)
plt.ylabel(y.split()[0]+' '+y.split()[1] if len(y.split()) > 1 else y )
plt.show()
Correlation Matrix:
plt.figure(1 , figsize = (15 , 6))
for gender in ['Male' , 'Female']:
plt.scatter(x = 'Age' , y = 'Annual Income (k$)' , data = df[df['Gender'] == gender] ,
s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Age'), plt.ylabel('Annual Income (k$)')
plt.title('Age vs Annual Income w.r.t Gender')
plt.legend()
plt.show()
plt.figure(1 , figsize = (15 , 6))
for gender in ['Male' , 'Female']:
plt.scatter(x = 'Annual Income (k$)',y = 'Spending Score (1-100)' ,
data = df[df['Gender'] == gender] ,s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Annual Income (k$)'), plt.ylabel('Spending Score (1-100)')
plt.title('Annual Income vs Spending Score w.r.t Gender')
plt.legend()
plt.show()
plt.figure(1 , figsize = (15 , 7))
n = 0
for cols in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
n += 1
plt.subplot(1 , 3 , n)
plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
sns.violinplot(x = cols , y = 'Gender' , data = df , palette = 'vlag')
sns.swarmplot(x = cols , y = 'Gender' , data = df)
plt.ylabel('Gender' if n == 1 else '')
plt.title('Boxplots & Swarmplots' if n == 2 else '')
plt.show()
Kmeans Clustering
- Age and spending score
##Age and spending Score
X1 = df[['Age' , 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1 , 11):
algorithm = (KMeans(n_clusters = n ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X1)
inertia.append(algorithm.inertia_)
plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(1 , 11) , inertia , 'o')
plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)
plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')
plt.show()
algorithm = (KMeans(n_clusters = 4 ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X1)
labels1 = algorithm.labels_
centroids1 = algorithm.cluster_centers_
h = 0.02
x_min, x_max = X1[:, 0].min() - 1, X1[:, 0].max() + 1
y_min, y_max = X1[:, 1].min() - 1, X1[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])
plt.figure(1 , figsize = (15 , 7) )
plt.clf()
Z = Z.reshape(xx.shape)
plt.imshow(Z , interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap = plt.cm.Pastel2, aspect = 'auto', origin='lower')
plt.scatter( x = 'Age' ,y = 'Spending Score (1-100)' , data = df , c = labels1 ,
s = 200 )
plt.scatter(x = centroids1[: , 0] , y = centroids1[: , 1] , s = 300 , c = 'red' , alpha = 0.5)
plt.ylabel('Spending Score (1-100)') , plt.xlabel('Age')
plt.show()
- Annual Income and Spending Score
##Annual Income and spending Score
X2 = df[['Annual Income (k$)' , 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1 , 11):
algorithm = (KMeans(n_clusters = n ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X2)
inertia.append(algorithm.inertia_)
plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(1 , 11) , inertia , 'o')
plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)
plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')
plt.show()
algorithm = (KMeans(n_clusters = 5 ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X2)
labels2 = algorithm.labels_
centroids2 = algorithm.cluster_centers_
h = 0.02
x_min, x_max = X2[:, 0].min() - 1, X2[:, 0].max() + 1
y_min, y_max = X2[:, 1].min() - 1, X2[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z2 = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])
plt.figure(1 , figsize = (15 , 7) )
plt.clf()
Z2 = Z2.reshape(xx.shape)
plt.imshow(Z2 , interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap = plt.cm.Pastel2, aspect = 'auto', origin='lower')
plt.scatter( x = 'Annual Income (k$)' ,y = 'Spending Score (1-100)' , data = df , c = labels2 ,
s = 200 )
plt.scatter(x = centroids2[: , 0] , y = centroids2[: , 1] , s = 300 , c = 'red' , alpha = 0.5)
plt.ylabel('Spending Score (1-100)') , plt.xlabel('Annual Income (k$)')
plt.show()
All Code:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
import plotly as py
import plotly.graph_objs as go
from sklearn.cluster import KMeans
import warnings
import os
warnings.filterwarnings("ignore")
py.offline.init_notebook_mode(connected = True)
#print(os.listdir("../input"))
df = pd.read_csv(r'Mall_Customers.csv')
plt.style.use('fivethirtyeight')
plt.figure(1 , figsize = (15 , 6))
n = 0
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
n += 1
plt.subplot(1 , 3 , n)
plt.subplots_adjust(hspace =0.5 , wspace = 0.5)
sns.distplot(df[x] , bins = 20)
plt.title('Distplot of {}'.format(x))
plt.show()
plt.figure(1 , figsize = (15 , 5))
sns.countplot(y = 'Gender' , data = df)
plt.show()
plt.figure(1 , figsize = (15 , 7))
n = 0
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
for y in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
n += 1
plt.subplot(3 , 3 , n)
plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
sns.regplot(x = x , y = y , data = df)
plt.ylabel(y.split()[0]+' '+y.split()[1] if len(y.split()) > 1 else y )
plt.show()
plt.figure(1 , figsize = (15 , 6))
for gender in ['Male' , 'Female']:
plt.scatter(x = 'Age' , y = 'Annual Income (k$)' , data = df[df['Gender'] == gender] ,
s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Age'), plt.ylabel('Annual Income (k$)')
plt.title('Age vs Annual Income w.r.t Gender')
plt.legend()
plt.show()
plt.figure(1 , figsize = (15 , 6))
for gender in ['Male' , 'Female']:
plt.scatter(x = 'Annual Income (k$)',y = 'Spending Score (1-100)' ,
data = df[df['Gender'] == gender] ,s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Annual Income (k$)'), plt.ylabel('Spending Score (1-100)')
plt.title('Annual Income vs Spending Score w.r.t Gender')
plt.legend()
plt.show()
plt.figure(1 , figsize = (15 , 7))
n = 0
for cols in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
n += 1
plt.subplot(1 , 3 , n)
plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
sns.violinplot(x = cols , y = 'Gender' , data = df , palette = 'vlag')
sns.swarmplot(x = cols , y = 'Gender' , data = df)
plt.ylabel('Gender' if n == 1 else '')
plt.title('Boxplots & Swarmplots' if n == 2 else '')
plt.show()
'''Age and spending Score'''
X1 = df[['Age' , 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1 , 11):
algorithm = (KMeans(n_clusters = n ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X1)
inertia.append(algorithm.inertia_)
plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(1 , 11) , inertia , 'o')
plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)
plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')
plt.show()
algorithm = (KMeans(n_clusters = 4 ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X1)
labels1 = algorithm.labels_
centroids1 = algorithm.cluster_centers_
h = 0.02
x_min, x_max = X1[:, 0].min() - 1, X1[:, 0].max() + 1
y_min, y_max = X1[:, 1].min() - 1, X1[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])
plt.figure(1 , figsize = (15 , 7) )
plt.clf()
Z = Z.reshape(xx.shape)
plt.imshow(Z , interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap = plt.cm.Pastel2, aspect = 'auto', origin='lower')
plt.scatter( x = 'Age' ,y = 'Spending Score (1-100)' , data = df , c = labels1 ,
s = 200 )
plt.scatter(x = centroids1[: , 0] , y = centroids1[: , 1] , s = 300 , c = 'red' , alpha = 0.5)
plt.ylabel('Spending Score (1-100)') , plt.xlabel('Age')
plt.show()
'''Annual Income and spending Score'''
X2 = df[['Annual Income (k$)' , 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1 , 11):
algorithm = (KMeans(n_clusters = n ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X2)
inertia.append(algorithm.inertia_)
plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(1 , 11) , inertia , 'o')
plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)
plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')
plt.show()
algorithm = (KMeans(n_clusters = 5 ,init='k-means++', n_init = 10 ,max_iter=300,
tol=0.0001, random_state= 111 , algorithm='elkan') )
algorithm.fit(X2)
labels2 = algorithm.labels_
centroids2 = algorithm.cluster_centers_
h = 0.02
x_min, x_max = X2[:, 0].min() - 1, X2[:, 0].max() + 1
y_min, y_max = X2[:, 1].min() - 1, X2[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z2 = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])
plt.figure(1 , figsize = (15 , 7) )
plt.clf()
Z2 = Z2.reshape(xx.shape)
plt.imshow(Z2 , interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap = plt.cm.Pastel2, aspect = 'auto', origin='lower')
plt.scatter( x = 'Annual Income (k$)' ,y = 'Spending Score (1-100)' , data = df , c = labels2 ,
s = 200 )
plt.scatter(x = centroids2[: , 0] , y = centroids2[: , 1] , s = 300 , c = 'red' , alpha = 0.5)
plt.ylabel('Spending Score (1-100)') , plt.xlabel('Annual Income (k$)')
plt.show()