Customer Segmentation using K-Means Unsupervised learning Algorithm
Notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
import pandas_profiling
import plotly.offline as po
import plotly.graph_objs as go
%matplotlib inline
Exploratory Data Analysis (EDA)
pandas_profiling.ProfileReport(pd.read_csv('/content/Mall_Customers.csv'))
#import Customer Churn Dataset
ds = pd.read_csv("/content/Mall_Customers.csv")
#Check the shape of the datasets
ds.shape
(200, 5)
ds.dtypes
CustomerID int64 Gender object Age int64 Annual Income (k$) int64 Spending Score (1-100) int64 dtype: object
#View the data head
ds.head()
Data Cleaning and manipulation
ds.isnull().sum()
CustomerID 0 Gender 0 Age 0 Annual Income (k$) 0 Spending Score (1-100) 0 dtype: int64
ds.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 200 entries, 0 to 199 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 CustomerID 200 non-null int64 1 Gender 200 non-null object 2 Age 200 non-null int64 3 Annual Income (k$) 200 non-null int64 4 Spending Score (1-100) 200 non-null int64 dtypes: int64(4), object(1) memory usage: 7.9+ KB
#Specifying our data for x in terms of price and quantity
x= ds.iloc[:,[2,4]].values
Visualize the data
plot_by_labels = ds["Age"].value_counts().keys().tolist()
plot_by_values = ds["Annual Income (k$)"].value_counts().values.tolist()
plot_data= [
go.Pie(labels = plot_by_labels,
values = plot_by_values,
marker = dict(colors = [ 'Teal' ,'Grey'],
line = dict(color = "white",
width = 1.5)),
rotation = 90,
hoverinfo = "label+value+text",
hole = .6)
]
plot_layout = go.Layout(dict(title = "Ages with percentage of total Annual Income",
plot_bgcolor = "rgb(243,243,243)",
paper_bgcolor = "rgb(243,243,243)",))
fig = go.Figure(data=plot_data, layout=plot_layout)
po.iplot(fig)
# Choosing the Annual Income and Spending score column
x= ds.iloc[:,[3,4]].values
x
array([[ 15, 39],
[ 15, 81],
[ 16, 6],
[ 16, 77],
[ 17, 40],
[ 17, 76],
[ 18, 6],
[ 18, 94],
[ 19, 3],
[ 19, 72],
[ 19, 14],
[ 19, 99],
[ 20, 15],
[ 20, 77],
[ 20, 13],
[ 20, 79],
[ 21, 35],
[ 21, 66],
[ 23, 29],
[ 23, 98],
[ 24, 35],
[ 24, 73],
[ 25, 5],
[ 25, 73],
[ 28, 14],
[ 28, 82],
[ 28, 32],
[ 28, 61],
[ 29, 31],
[ 29, 87],
[ 30, 4],
[ 30, 73],
[ 33, 4],
[ 33, 92],
[ 33, 14],
[ 33, 81],
[ 34, 17],
[ 34, 73],
[ 37, 26],
[ 37, 75],
[ 38, 35],
[ 38, 92],
[ 39, 36],
[ 39, 61],
[ 39, 28],
[ 39, 65],
[ 40, 55],
[ 40, 47],
[ 40, 42],
[ 40, 42],
[ 42, 52],
[ 42, 60],
[ 43, 54],
[ 43, 60],
[ 43, 45],
[ 43, 41],
[ 44, 50],
[ 44, 46],
[ 46, 51],
[ 46, 46],
[ 46, 56],
[ 46, 55],
[ 47, 52],
[ 47, 59],
[ 48, 51],
[ 48, 59],
[ 48, 50],
[ 48, 48],
[ 48, 59],
[ 48, 47],
[ 49, 55],
[ 49, 42],
[ 50, 49],
[ 50, 56],
[ 54, 47],
[ 54, 54],
[ 54, 53],
[ 54, 48],
[ 54, 52],
[ 54, 42],
[ 54, 51],
[ 54, 55],
[ 54, 41],
[ 54, 44],
[ 54, 57],
[ 54, 46],
[ 57, 58],
[ 57, 55],
[ 58, 60],
[ 58, 46],
[ 59, 55],
[ 59, 41],
[ 60, 49],
[ 60, 40],
[ 60, 42],
[ 60, 52],
[ 60, 47],
[ 60, 50],
[ 61, 42],
[ 61, 49],
[ 62, 41],
[ 62, 48],
[ 62, 59],
[ 62, 55],
[ 62, 56],
[ 62, 42],
[ 63, 50],
[ 63, 46],
[ 63, 43],
[ 63, 48],
[ 63, 52],
[ 63, 54],
[ 64, 42],
[ 64, 46],
[ 65, 48],
[ 65, 50],
[ 65, 43],
[ 65, 59],
[ 67, 43],
[ 67, 57],
[ 67, 56],
[ 67, 40],
[ 69, 58],
[ 69, 91],
[ 70, 29],
[ 70, 77],
[ 71, 35],
[ 71, 95],
[ 71, 11],
[ 71, 75],
[ 71, 9],
[ 71, 75],
[ 72, 34],
[ 72, 71],
[ 73, 5],
[ 73, 88],
[ 73, 7],
[ 73, 73],
[ 74, 10],
[ 74, 72],
[ 75, 5],
[ 75, 93],
[ 76, 40],
[ 76, 87],
[ 77, 12],
[ 77, 97],
[ 77, 36],
[ 77, 74],
[ 78, 22],
[ 78, 90],
[ 78, 17],
[ 78, 88],
[ 78, 20],
[ 78, 76],
[ 78, 16],
[ 78, 89],
[ 78, 1],
[ 78, 78],
[ 78, 1],
[ 78, 73],
[ 79, 35],
[ 79, 83],
[ 81, 5],
[ 81, 93],
[ 85, 26],
[ 85, 75],
[ 86, 20],
[ 86, 95],
[ 87, 27],
[ 87, 63],
[ 87, 13],
[ 87, 75],
[ 87, 10],
[ 87, 92],
[ 88, 13],
[ 88, 86],
[ 88, 15],
[ 88, 69],
[ 93, 14],
[ 93, 90],
[ 97, 32],
[ 97, 86],
[ 98, 15],
[ 98, 88],
[ 99, 39],
[ 99, 97],
[101, 24],
[101, 68],
[103, 17],
[103, 85],
[103, 23],
[103, 69],
[113, 8],
[113, 91],
[120, 16],
[120, 79],
[126, 28],
[126, 74],
[137, 18],
[137, 83]])#Find optimal number of clusters using the Elbow method
from sklearn.cluster import KMeans
wcss=[]
for i in range(1,6):
kmeans =KMeans(n_clusters= i, init='k-means++', random_state=0)
kmeans.fit(x)
wcss.append(kmeans.inertia_)
/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning /usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning /usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning /usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning /usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
#Annotate point showing the optimum number of clusters
plt.plot(range(1,6), wcss)
plt.title('The elbow method')
plt.xlabel('No. of clusters')
plt.ylabel('WCSS Values')
plt.annotate('Optimum No. of Clusters',
xy=(5,50000), xycoords='data',
xytext=(5.0, 150000),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3",color='red'))
plt.show()
Double-click (or enter) to edit
Training the K-Means Clustering Model
kmeansmodel= KMeans(n_clusters = 5, init='k-means++', random_state=0)
y_kmeans = kmeansmodel.fit_predict(x)
/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
#return a label for each data point based on their cluster
print(y_kmeans)
[4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 1 4 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 0 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2]
Data Visualization