Project Title

Customer Segmentation using K-Means Clustering with Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Unsupervised Learning | Data Science | Customer Analytics

Project Level

Beginner to Intermediate

Project Objective

Businesses collect large amounts of customer data but often struggle to understand different customer groups. The objective of this project is to build an intelligent Machine Learning model using the K-Means Clustering algorithm to segment customers into different groups based on their purchasing behavior, income, spending patterns, and demographic characteristics.

Customer segmentation helps businesses develop targeted marketing strategies, improve customer satisfaction, and increase revenue through personalized services.

Session Access Link

Project Training Session

Session Link:

Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand Unsupervised Machine Learning concepts.
• Learn how customer segmentation systems work.
• Understand clustering algorithms and their applications.
• Perform customer behavior analysis.
• Visualize customer groups using data analytics.
• Apply K-Means Clustering to real-world datasets.
• Determine the optimal number of clusters.
• Interpret customer segments for business decision-making.
• Build a real-world customer segmentation system.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn

• NumPy

• Pandas

Data Visualization Libraries

• Matplotlib

• Seaborn

Development Platforms

• Google Colab

• Jupyter Notebook

• VS Code (Optional)

Data Storage

• CSV Files

• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Customer Segmentation Dataset containing:

• Customer ID
• Gender
• Age
• Annual Income
• Spending Score
• Purchase Frequency (Optional)
• Membership Status (Optional)

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Customer Age Distribution
• Income Distribution Analysis
• Spending Score Analysis
• Income vs Spending Score Visualization
• Pair Plots
• Correlation Analysis
• Cluster Visualization

Benefits:

• Understand customer purchasing behavior.
• Identify high-value and low-value customers.
• Discover hidden customer patterns.

Phase 4: Feature Engineering

Students will prepare data for clustering using:

• Feature Scaling
• Standardization
• Data Normalization
• Dimensionality Reduction (Optional)

Benefits:

• Improves clustering quality.
• Creates well-separated customer groups.
• Enhances model performance.

Phase 5: Model Building

Students will train clustering models such as:

K-Means Clustering

• Most popular customer segmentation algorithm.
• Groups customers with similar characteristics.

Hierarchical Clustering (Optional)

• Creates hierarchical customer groups.
• Useful for comparison.

DBSCAN (Optional)

• Density-based clustering algorithm.
• Detects outliers and unusual customer behavior.

Phase 6: Model Evaluation

Students will evaluate clustering performance using:

• Elbow Method
• Within Cluster Sum of Squares (WCSS)
• Silhouette Score
• Cluster Visualization

Expected Result:

3–7 meaningful customer segments depending on dataset characteristics.

Phase 7: Customer Segmentation System

Students will create a system where users can:

• Input customer information.
• Click Analyze.
• Receive output:

Examples:

• Premium Customers
• Regular Customers
• High Income, Low Spending Customers
• Budget Customers
• Potential Loyal Customers

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)

• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

Project Report (PDF)

Screenshots

• Dataset Import
• Data Cleaning
• Exploratory Data Analysis
• Elbow Method Graph
• Cluster Visualization
• Customer Segment Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Clustering Model (.pkl file)

• Cluster Visualization Outputs

• Customer Segment Reports

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

CustomerSegmentation_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.