SYildiz
1/10/2019 - 7:20 AM
Customer Segmentation with K-Means
Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.
import random
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
%matplotlib inline
#Sample Data
!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv
#Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv.
import pandas as pd
cust_df = pd.read_csv("Cust_Segmentation.csv")
cust_df.head()
#Pre-processing
#As you can see, Address in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical
#variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering.
df = cust_df.drop('Address', axis=1)
df.head()
#Normalizing over the standard deviation
#Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that
#helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use
#StandardScaler() to normalize our dataset
from sklearn.preprocessing import StandardScaler
X = df.values[:,1:]
X = np.nan_to_num(X)
Clus_dataSet = StandardScaler().fit_transform(X)
Clus_dataSet
#Modeling
#In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer
#group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means
#clustering we can do all this process much easier.
#Lets apply k-means on our dataset, and take look at cluster labels.
clusterNum = 3
k_means = KMeans(init = "k-means++", n_clusters = clusterNum, n_init = 12)
k_means.fit(X)
labels = k_means.labels_
print(labels)
#Insights
#We assign the labels to each row in dataframe.
df["Clus_km"] = labels
df.head(5)
#We can easily check the centroid values by averaging the features in each cluster.
df.groupby('Clus_km').mean()
#Now, lets look at the distribution of customers based on their age and income:
area = np.pi * ( X[:, 1])**2
plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)
plt.xlabel('Age', fontsize=18)
plt.ylabel('Income', fontsize=16)
plt.show()
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(1, figsize=(8, 6))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
plt.cla()
# plt.ylabel('Age', fontsize=18)
# plt.xlabel('Income', fontsize=16)
# plt.zlabel('Education', fontsize=16)
ax.set_xlabel('Education')
ax.set_ylabel('Age')
ax.set_zlabel('Income')
ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))
#k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each
#cluster are similar to each other demographically. Now we can create a profile for each group, considering the common
#characteristics of each cluster. For example, the 3 clusters can be:
#AFFLUENT, EDUCATED AND OLD AGED
#MIDDLE AGED AND MIDDLE INCOME
#YOUNG AND LOW INCOME