Customer Segmentation for E-commerce using KMeans (Python Flask)

Understanding your customers is key to growing your online business. This project leverages
KMeans Clustering
, a machine learning algorithm, to automatically segment your customers based on shopping behavior like
spending score, income, and shopping frequency
Built using
Python, Flask, and Tailwind CSS
, this project is perfect for:
Final-year students in
Data Science
or
Machine Learning
E-commerce startups
or marketers looking to understand customer types
Beginners learning
Flask web apps
+
ML integration
✅
Features of This Project:
🧮
Customer Clustering
using KMeans Algorithm
📊
Interactive Dashboards
with visualizations:
Elbow Method Plot
PCA 2D and 3D Graphs
Pie Chart & Bar Chart for Customer Type Distribution
🔍
Silhouette Score
to measure clustering performance
🧑‍💼
Easy-to-understand UI
for shop owners
👨‍💻
Toggle for Data Science View
(Detailed technical plots)
⬇️
CSV Export
of clustered customers with segment labels:
Budget Shoppers
High Spenders
Occasional Buyers
Loyal Customers
Technologies Used:
Python 3.x
Flask Web Framework
Pandas, NumPy, Scikit-learn
Matplotlib, Seaborn
Tailwind CSS (for UI)
HTML5 / Jinja2
📝 How It Works:
User uploads a CSV file with customer data.
Backend preprocesses it and applies
KMeans clustering
Results are shown visually with charts and segment labels.
Optionally, users can download the analyzed data for marketing insights.
What You Get in the ZIP:
Complete source code (
app.py
, templates, static files)
Pre-built HTML with Tailwind UI
Dataset and sample input
Readme + setup instructions
Labeled output CSV
Project Report + PPT
Download Full Project with Source Code (ZIP)
Demo Video

Detailed Project Description

Customer Segmentation for E-commerce using KMeans (Python Flask) provides a complete buy-and-sell workflow: browsing products, managing a cart, placing orders and, on the admin side, managing catalogue, stock and orders.

How It Works
1. Users register/login and browse products by category or search.
2. Selected items go to a cart; checkout collects address and payment details and creates an order.
3. Admins manage products, stock, prices, orders and reports from a separate panel.
4. Order status updates are shown to the customer.

Technology Roles
- Python
- Flask
- Machine Learning

Implementation Steps
1. Define requirements and draw the ER / class diagram and screen flow.
2. Set up the environment (Python, Flask, Machine Learning) and create the project skeleton.
3. Design the database schema / data model and seed it with sample data.
4. Implement the core modules with validation and role-based access where needed.
5. Build the user interface and connect it to the backend / data layer.
6. Test each module (unit + manual), fix edge cases and secure inputs (SQL injection, XSS, auth).
7. Prepare documentation, screenshots and a demo build.

Key Features
- Product catalogue and search
- Cart and checkout
- Order tracking
- Admin panel
- Reports

Expected Output
On completion the student will have a working storefront with an admin back-office and order history.

Deliverables
- Complete source code with comments
- Database schema / data model and screen flow
- Project report with screenshots and results
- Demonstration-ready build