This document discusses customer segmentation using K-means clustering. The objectives are to identify meaningful customer segments, understand customer behavior, and optimize marketing. The methodology applies K-means clustering and DBSCAN algorithms to customer data to divide customers into clusters. The results are evaluated using silhouette scores to validate the optimal number of clusters. Key customer segments are identified that can be targeted differently, such as high spenders. Future work may include additional features, algorithms, and predictive analytics to develop dynamic customer segmentation.

1. FOUNDATIONS OF DATA SCIENCE
Course Based Project
CUSTOMER SEGMENTATION USING K-MEANS CLUSTERING
NAME: Yelubandi Aravind
Course: M.Tech (Embedded Systems)
Roll No: UCC22ECES11
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National Institute of Electronics and
Information Technology
Ministry of Electronics and Information Technology, Government of India
Calicut, Kerala-673601

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Background of the project
 Nowadays, success in business relies on generating innovative ideas, especially because there are
many potential customers who are unsure about what products or services to choose.
 This is where machine learning comes into play, by applying various algorithms, we can uncover
hidden patterns in data, enabling better decision making.
 To achieve this, we can use a clustering technique called the K-means and DBSCAN algorithms, which
divides customers into clusters based on similarities.
 To determine the optimal number of clusters, we can utilize the elbow method, which helps us find
the right balance between capturing enough distinct segments while avoiding excessive complexity.
 we doing Silhouette Score validation for checking number of clusters determined through elbow
method.

3. Literature Survey
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S.No CITATION KEY IDEA/APPROACH LIMITATIONS/REMARKS
1
R. Gupta, A. Verma and H. O. Topal,
"Customer Segmentation of Indian
restaurants on the basis of geographical
locations using Machine Learning," 2021
International Conference on Technological
Advancements and Innovations (ICTAI),
Tashkent, Uzbekistan, 2021, pp. 382-387,
doi: 10.1109/ICTAI53825.2021.9673153.
In this paper, They have
implemented the K-Means
clustering algorithm on the
dataset of all the restaurants
present in Bangalore based
in Python Language.
2
Tushar Kansal, Suraj Bahuguna, Vishal
Singh, Tanupriya Choudhury (June 2018).
Customer Segmentation using K-means
Clustering.
3
Mr. M. Sathyanarayana, S. Dhanish, P.
Shiva Kumar, A. Niranjan Reddy
(December 2022). Mall Customer
Segmentation Using Clustering Algorithm.

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S.No CITATION KEY IDEA/APPROACH LIMITATIONS/REMARKS
4
Hemashree Kilari, Sailesh
Edara, Guna Ratna Sai
Yarra, Dileep Varma
Gadhiraju (March 2022).
Customer Segmentation
using K-Means Clustering.
5
Pavithra M, Ayushman
Prashar, Abirami (July
2022). Maximizing Strategy
in Customer Segmentation
Using Different Clustering
Techniques.
6
Mathesh T, Sumathy G,
Maheshwari A ( May 2023).
A Machine Learning
approach to segment the
customers of online sales
data for better and efficient
marketing purposes.

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Problem Statement
 In Business the owners wants to know the data like which type of customers are coming for
purchasing. So, to get this type of data using dataset that they have is complex.
 The dataset that the Business units having is unsupervised data so it is complex to derive
required data from that dataset.
 To overcome this, In machine learning there is method called clustering which will divide
customers into different groups based on their purchasing history.
 The project aims to create a simple machine-learning model in Python for customers
segmentation using K-means and DBSCAN algorithms in clustering.
 The model will clustering algorithms based on important features present in the data set.
 It will handle complex data and capture relationships effectively.

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Objectives of the project
 Identify Meaningful Customer Segments.
 Understand Customer Preferences and Behavior.
 Personalize Marketing and Offerings.
 Improve Customer Retention and Loyalty.
 Optimize Marketing Resource Allocation.
 Enhance Customer Experience.
 Gain Competitive Advantage.
 Measure and Evaluate Cluster Quality.
 Provide Actionable Insights and Recommendations.
 Monitor and adapt.

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Scope of the Project
 Data Collection.
 Data Preprocessing.
 Feature Selection.
 Algorithm Selection.
 Clustering Implementation.
 Evaluation and Validation.
 Interpretation and Insights.
 Visualization and Reporting.
 Recommendations and Actionable Strategies.

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Tools & Resources
 Google Colab, platform was used for coding and model development.
 Python Programming language was utilized within the Google Colab environment.
 Pandas, NumPy, and Scikit-learn libraries were employed for data processing,
manipulation, and machine-learning tasks.
 Matplotlib and Seaborn libraries were used for data visualization within the Google
Colab Notebook.

9. Methodology
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10. Result
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Conclusion
customer segmentation using K-means and DBSCAN algorithms, along with the validation of the
segmentation through Silhouette score, provides businesses with a powerful framework for
understanding their customer base, identifying distinct customer groups, and making data-driven
decisions to optimize their marketing efforts.
 High-income, high-spending customers: Target them as they have more money to spend.
 High-income, low-spending customers: Engage with them by seeking feedback and improving
advertising to increase their spending.
 Average-income, average-spending customers: May or may not be beneficial to mall owners. It
depends on individual circumstances.
 Low-income, high-spending customers: Target them by offering affordable payment options like low-
cost EMI plans.
 Low-income, low-spending customers: Avoid targeting them as they have limited income and spend
less.

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Future Scope
 Feature Engineering: Explore and incorporate additional customer features or variables that can
provide richer insights for segmentation.
 Algorithm Enhancement: Assess their performance in customer segmentation and compare them with
the existing methods.
 Dynamic Segmentation: This could involve developing an automated system that continuously analyses
and updates clusters based on new data.
 Predictive Analytics: Apply predictive modelling techniques to forecast future customer behavior
within each segment.
 Multi-channel Analysis: Extend the segmentation analysis to incorporate multiple channels, such as
offline and online customer interactions.
 Segmentation Visualization: Develop visualizations and dashboards to effectively communicate the
segmented customer groups and their characteristics to stakeholders within the organization. This can
aid in decision-making, resource allocation, and marketing strategy formulation.

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References
1. V. Vijilesh, A. Harini, M. Hari Dharshini, R. Priyadharshini (May 2021). Customer Segmentation Using Machine
Learning. https://www.irjet.net/archives/V8/i5/IRJET-V8I5163.pdf
2. Tushar Kansal, Suraj Bahuguna, Vishal Singh, Tanupriya Choudhury (June 2018). Customer Segmentation
using K-means Clustering. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8769171&tag=1
3. Mr. M. Sathyanarayana, S. Dhanish, P. Shiva Kumar, A. Niranjan Reddy (December 2022). Mall Customer
Segmentation Using Clustering Algorithm. https://www.ijraset.com/research-paper/mall-customer-
segmentation-usingclustering-algorithm
4. Hemashree Kilari, Sailesh Edara, Guna Ratna Sai Yarra, Dileep Varma Gadhiraju (March 2022). Customer
Segmentation using K-Means Clustering. https://www.ijert.org/research/customer-segmentation-using-k-
meansclustering-IJERTV11IS030152.pdf
5. Pavithra M, Ayushman Prashar, Abirami (July 2022). Maximizing Strategy in Customer Segmentation Using
Different Clustering Techniques. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9774200
6. Mathesh T, Sumathy G, Maheshwari A ( May 2023). A Machine Learning approach to segment the customers
of online sales data for better and efficient marketing purposes.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10084339

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