CUSTOMER SEGMENTATION USING K-MEANS CLUSTERING
INTRODUCTION
Customer segmentation is the process of dividing customers into groups based on their behaviour and characteristics . This project uses K-Means Clustering to identify different types of customers based on features like age , income and spending habits . Businesses can use this improve marketing strategies and customer targeting.
OBJECTIVES
* To segment customers into groups .
* To identify customer behaviour patterns .
* To help businesses target customers effectively .
TOOLS & TECHNOLOGIES USED
* Python
* Pandas
* NumPy
* Matplotlib / Seaborn
* Scikit-learn
ENVIRONMENT SETUP
In [31]:
pip install pandas numpy matplotlib seaborn scikit-learn plotly
Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)
Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)
Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)
Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)
Requirement already satisfied: plotly in /usr/local/lib/python3.12/dist-packages (5.24.1)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)
Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.3)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)
Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)
Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)
Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)
Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.3)
Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)
Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from plotly) (9.1.4)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
IMPORT & UPLOAD FILE
In [2]:
from google.colab import files uploaded = files.upload()
Saving customer_segmentation.csv to customer_segmentation.csv
IMPORT LIBRARIES
In [32]:
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns
LOAD DATASET
In [4]:
df = pd.read_csv("customer_segmentation.csv")
df.head()Out[4]:
| ID | Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | ... | NumWebVisitsMonth | AcceptedCmp3 | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Z_CostContact | Z_Revenue | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5524 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 04-09-2012 | 58 | 635 | ... | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 1 |
| 1 | 2174 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 08-03-2014 | 38 | 11 | ... | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
| 2 | 4141 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 21-08-2013 | 26 | 426 | ... | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
| 3 | 6182 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 10-02-2014 | 26 | 11 | ... | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
| 4 | 5324 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 19-01-2014 | 94 | 173 | ... | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 0 |
5 rows × 29 columns
UNDERSTAND DATA
In [5]:
df.info() df.describe() df.isnull().sum()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ID 2240 non-null int64
1 Year_Birth 2240 non-null int64
2 Education 2240 non-null object
3 Marital_Status 2240 non-null object
4 Income 2216 non-null float64
5 Kidhome 2240 non-null int64
6 Teenhome 2240 non-null int64
7 Dt_Customer 2240 non-null object
8 Recency 2240 non-null int64
9 MntWines 2240 non-null int64
10 MntFruits 2240 non-null int64
11 MntMeatProducts 2240 non-null int64
12 MntFishProducts 2240 non-null int64
13 MntSweetProducts 2240 non-null int64
14 MntGoldProds 2240 non-null int64
15 NumDealsPurchases 2240 non-null int64
16 NumWebPurchases 2240 non-null int64
17 NumCatalogPurchases 2240 non-null int64
18 NumStorePurchases 2240 non-null int64
19 NumWebVisitsMonth 2240 non-null int64
20 AcceptedCmp3 2240 non-null int64
21 AcceptedCmp4 2240 non-null int64
22 AcceptedCmp5 2240 non-null int64
23 AcceptedCmp1 2240 non-null int64
24 AcceptedCmp2 2240 non-null int64
25 Complain 2240 non-null int64
26 Z_CostContact 2240 non-null int64
27 Z_Revenue 2240 non-null int64
28 Response 2240 non-null int64
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB
Out[5]:
| 0 | |
|---|---|
| ID | 0 |
| Year_Birth | 0 |
| Education | 0 |
| Marital_Status | 0 |
| Income | 24 |
| Kidhome | 0 |
| Teenhome | 0 |
| Dt_Customer | 0 |
| Recency | 0 |
| MntWines | 0 |
| MntFruits | 0 |
| MntMeatProducts | 0 |
| MntFishProducts | 0 |
| MntSweetProducts | 0 |
| MntGoldProds | 0 |
| NumDealsPurchases | 0 |
| NumWebPurchases | 0 |
| NumCatalogPurchases | 0 |
| NumStorePurchases | 0 |
| NumWebVisitsMonth | 0 |
| AcceptedCmp3 | 0 |
| AcceptedCmp4 | 0 |
| AcceptedCmp5 | 0 |
| AcceptedCmp1 | 0 |
| AcceptedCmp2 | 0 |
| Complain | 0 |
| Z_CostContact | 0 |
| Z_Revenue | 0 |
| Response | 0 |
CHECKING COLUMNS
In [8]:
print(df.columns)
Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',
'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',
'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response',
'Age', 'TotalSpending'],
dtype='object')
FEATURE SELECTION
In [13]:
df_selected = df[['Age','Income','TotalSpending']] df_selected.head()
Out[13]:
| Age | Income | TotalSpending | |
|---|---|---|---|
| 0 | 57 | 58138.0 | 1617 |
| 1 | 60 | 46344.0 | 27 |
| 2 | 49 | 71613.0 | 776 |
| 3 | 30 | 26646.0 | 53 |
| 4 | 33 | 58293.0 | 422 |
DATA CLEANING STEP
In [15]:
print("Before:" , df.shape)
df= df.dropna()
print("After:" , df.shape)Before: (2216, 31)
After: (2216, 31)
DATA SCALING
In [23]:
from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df_selected = df_selected.dropna() # Ensure df_selected is free of NaNs df_scaled = scaler.fit_transform(df_selected) print(df_scaled[:5])
[[ 0.98644293 0.2340627 1.67548812]
[ 1.23680074 -0.23455948 -0.96235832]
[ 0.31882209 0.76947764 0.28024985]
[-1.2667774 -1.01723878 -0.91922372]
[-1.01641959 0.24022146 -0.30704427]]
ELBOW METHOD
In [29]:
from sklearn.cluster import KMeans
inertia = []
for k in range(1,11):
kmeans = KMeans(n_clusters=k,random_state=42)
kmeans.fit(df_scaled)
inertia.append(kmeans.inertia_)
plt.plot(range(1,11),inertia,marker='o')
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.show()
K-MEANS CLUSTERING
In [26]:
kmeans = KMeans(n_clusters=5,random_state=42) df['Cluster'] = kmeans.fit_predict(df_scaled) df.head()
Out[26]:
| ID | Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | ... | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Z_CostContact | Z_Revenue | Response | Age | TotalSpending | Cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5524 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 04-09-2012 | 58 | 635 | ... | 0 | 0 | 0 | 0 | 3 | 11 | 1 | 57 | 1617 | 4 |
| 1 | 2174 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 08-03-2014 | 38 | 11 | ... | 0 | 0 | 0 | 0 | 3 | 11 | 0 | 60 | 27 | 2 |
| 2 | 4141 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 21-08-2013 | 26 | 426 | ... | 0 | 0 | 0 | 0 | 3 | 11 | 0 | 49 | 776 | 4 |
| 3 | 6182 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 10-02-2014 | 26 | 11 | ... | 0 | 0 | 0 | 0 | 3 | 11 | 0 | 30 | 53 | 0 |
| 4 | 5324 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 19-01-2014 | 94 | 173 | ... | 0 | 0 | 0 | 0 | 3 | 11 | 0 | 33 | 422 | 0 |
5 rows × 32 columns
DATA VISUALIZATION
2D PLOT VISUALIZATION
In [27]:
plt.scatter(df['Income'],df['TotalSpending'],c=df['Cluster'],cmap='rainbow')
plt.xlabel('Income')
plt.ylabel('Total Spending')
plt.title("Customer Segmentation")
plt.show()
3D PLOT VISUALIZATION
In [33]:
from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt
In [35]:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111,projection='3d')
ax.scatter(df['Age'],df['Income'],df['TotalSpending'],c=df['Cluster'])
ax.set_xlabel('Age')
ax.set_ylabel('Income')
ax.set_zlabel('Total Spending')
ax.set_title('Customer Segmentation')
plt.show()
INTERACTIVE PLOT
In [36]:
import plotly.express as px fig = px.scatter_3d(df,x='Age',y='Income',z='TotalSpending',color='Cluster',symbol='Cluster', title ='3D Customer Segmentation') fig.show()
ANALYZE CLUSTERS
In [30]:
df.groupby('Cluster').mean(numeric_only=True)Out[30]:
| ID | Year_Birth | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | MntFishProducts | ... | AcceptedCmp4 | AcceptedCmp5 | AcceptedCmp1 | AcceptedCmp2 | Complain | Z_CostContact | Z_Revenue | Response | Age | TotalSpending | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cluster | |||||||||||||||||||||
| 0 | 5570.913520 | 1977.404385 | 34228.068210 | 0.811206 | 0.327649 | 48.064555 | 63.939099 | 7.373934 | 36.423873 | 10.886724 | ... | 0.010962 | 0.000000 | 0.003654 | 0.000000 | 0.009744 | 3.0 | 11.0 | 0.113276 | 36.595615 | 148.496955 |
| 1 | 5486.571744 | 1977.046358 | 73664.443709 | 0.134658 | 0.381898 | 49.441501 | 621.273731 | 58.704194 | 383.571744 | 81.536424 | ... | 0.152318 | 0.211921 | 0.165563 | 0.035320 | 0.004415 | 3.0 | 11.0 | 0.245033 | 36.953642 | 1280.399558 |
| 2 | 5646.707407 | 1958.190741 | 45456.940741 | 0.437037 | 0.901852 | 49.811111 | 153.092593 | 9.600000 | 50.092593 | 13.575926 | ... | 0.062963 | 0.000000 | 0.007407 | 0.007407 | 0.016667 | 3.0 | 11.0 | 0.079630 | 55.809259 | 264.635185 |
| 3 | 9432.000000 | 1977.000000 | 666666.000000 | 1.000000 | 0.000000 | 23.000000 | 9.000000 | 14.000000 | 18.000000 | 8.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.0 | 11.0 | 0.000000 | 37.000000 | 62.000000 |
| 4 | 5650.872818 | 1956.246883 | 72556.753117 | 0.037406 | 0.476309 | 49.458853 | 647.064838 | 51.271820 | 347.463840 | 75.291771 | ... | 0.129676 | 0.164589 | 0.149626 | 0.024938 | 0.004988 | 3.0 | 11.0 | 0.214464 | 57.753117 | 1247.822943 |
5 rows × 28 columns
CUSTOMER SEGMENTATION INSIGHTS
Cluster 0 - Low Value Customers
* Income : Medium (~34k)
* Spending : Low ( very low product purchases )
* Recency : High ( not active recently )
Insight : These customers spend very little and are not highly engaged . They are low - value customers .
Business Strategy :
* Offer discounts & Promotions .
* Use email marketing to re-engage .
Cluster 1 - High Value Customers (VIP)
* Income : High ( ~73k)
* Spending : Very High ( high across all products)
* Recency : Moderate
Insight : These are your best customers - high income and high spending .
Business Strategy :
* Provide premium offers .
* Loyality programs .
* Exclusive deals .
Cluster 2 - Moderate Customers
* Income : Medium (~45k)
* Spending : Moderate
* Recency : Average
Insights : These customers are average - neither high nor low spenders .
Business Strategy :
* Upselling strategies .
* Personalized recommendations .
Cluster 3 - Very High Income But Low Spending
* Income : Extremely High (~666k)
* Spending : Very Low
* Recency : Very Low ( recently active )
Insight : This looks like a outlier or special case . High Income but not spending much .
Business Strategy :
* Target with premium marketing.
* Understand why they are not spending.
Cluster 4 - Active High Spenders
* Income : High (~72k)
* Spending : Very High
* Recency : Good Engagement
Insights : These are active loyal customers who frequently purchase .
Business Strategy :
* Retain them with rewards .
* Offer subscriptions or memberships .
Final Summary :
* Cluster 1 - 4 : High-Value Customers .
* Cluster 0 : Low-Value Customers .
* Cluster 2 : Potential Growth Customers .
* Cluster 3 : Outlier / Special Segment .
CONCLUSION
The clustering model successfully segmented customers into distinct groups based on income and spending behaviour . High-Value customers were identified along with low-engagement groups and potential growth segments . These insights can help businesses design targeted marketing strategies , improve customer retention and increase overall profitability .