FuzzySegment Pro

Intelligent Customer Profiling System using Fuzzy C-Means Clustering

---

Repository Structure

├── project/           # Main implementation code
│   ├── src/          # Core Python modules
│   ├── streamlit_app/# Web dashboard
│   ├── notebooks/    # Analysis scripts
│   ├── data/         # Dataset files
│   └── README.md     # Project documentation
│
└── report/           # (Coming soon) Technical report and presentation

---

Getting Started

Navigate to the project/ folder for full documentation and setup instructions.

cd project
conda create -n fuzzysegment python=3.10 -y
conda activate fuzzysegment
pip install -r requirements.txt
streamlit run streamlit_app/app.py

---

What is FuzzySegment Pro?

A customer segmentation tool that uses Fuzzy C-Means clustering to capture multi-dimensional customer behavior, going beyond traditional K-Means' single-category assignments.

Key Features:

• Soft clustering with membership degrees
• Multi-dimensional customer profiling
• Interactive Streamlit dashboard
• Comprehensive fuzzy validation metrics
• K-Means comparison analysis

---

Results Preview

• 793 customers analyzed from Superstore dataset
• 20-40% identified as multi-dimensional (missed by K-Means)
• 5 fuzzy metrics for cluster quality validation
• Real-time visualization of membership degrees

---

Links

• GitHub: https://github.com/BV-Tech-Team/FuzzySegment-Pro
• Full Documentation: project/README.md

---

Team

BV Tech Team

---

Built with Fuzzy C-Means & Granular Computing