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Customer Segmentation AI Dashboard

A Machine Learning project that uses K-Means Clustering to segment retail store customers based on their purchasing behavior.

The project also includes an interactive dashboard web application built using Flask and JavaScript where users can predict customer segments in real time.

🚀 Live Demo: https://customer-segmentation-dashboard-yiua.onrender.com

Project Overview

This project analyzes customer data from a mall dataset and divides customers into meaningful groups based on:

  • Annual Income
  • Spending Score

Customer segmentation helps businesses understand their audience and create targeted marketing strategies.

Tech Stack

| Category               | Technologies                          |
|------------------------|---------------------------------------|
| Programming Language   | Python                                |
| Machine Learning       | Scikit-learn (K-Means Clustering)     |
| Data Processing        | Pandas, NumPy                         |
| Data Visualization     | Matplotlib, Seaborn, Chart.js         |
| Backend                | Flask                                 |
| Frontend               | HTML, CSS, JavaScript                 |
| Deployment             | Render                                |
| Version Control        | Git & GitHub                          |

Project Folder Structure

Customer_Segmentation/
│
├── backend/
│   └── app.py                        # Flask backend API
│
<<<<<<< HEAD
├── frontend/
│   └── index.html                    # Web dashboard UI
│
├── Customer_Segmentation.ipynb       # Jupyter notebook for ML model
├── Mall_Customers.csv                # Dataset used for clustering
├── kmeans_model.pkl                  # Saved trained ML model
├── requirements.txt                  # Python dependencies
└── README.md                         # Project documentation

Dataset Information

The dataset used in this project is Mall_Customers.csv.

| Feature                | Description                       |
|------------------------|-----------------------------------|
| CustomerID             | Unique ID of the customer         |
| Gender                 | Male or Female                    |
| Age                    | Customer age                      |
| Annual Income (k$)     | Customer income                   |
| Spending Score (1-100) | Spending behavior score           |

For clustering, we selected:

  • Annual Income (k$)
  • Spending Score (1-100)

Machine Learning Workflow

| Step               | Description                              |
|--------------------|------------------------------------------|
| Data Loading       | Dataset loaded using Pandas              |
| Feature Selection  | Selected Income & Spending Score         |
| Feature Scaling    | StandardScaler used                      |
| Finding Optimal K  | Elbow Method                             |
| Model Training     | K-Means clustering applied               |
| Cluster Assignment | Customers assigned cluster labels        |
| Visualization      | Scatter plot used to visualize clusters  |

<<<<<<< HEAD

Step Description
Data Loading Dataset loaded using Pandas
Feature Selection Selected Income & Spending Score
Feature Scaling StandardScaler used
Finding Optimal K Elbow Method
Model Training K-Means clustering applied
Cluster Assignment Customers assigned cluster labels
Visualization Scatter plot used to visualize clusters

Customer Segments Identified

The algorithm identified 5 customer segments:

| Cluster   | Customer Type                    |
|-----------|----------------------------------|
| Cluster 0 | Low Income  – Low Spending       |
| Cluster 1 | Low Income  – High Spending      |
| Cluster 2 | Average Customers                |
| Cluster 3 | High Income – Low Spending       |
| Cluster 4 | High Income – High Spending      |

Web Dashboard Features

| Feature                | Description                              |
|------------------------|------------------------------------------|
| Prediction Panel       | Enter customer income & spending score   |
| Real-time Prediction   | Predict customer cluster instantly       |
| Cluster Visualization  | Scatter graph showing all clusters       |
| ML API                 | Flask backend serving predictions        |
| Interactive UI         | Responsive dashboard layout              |

How To Run The Project

1. Clone Repository

git clone https://github.com/ravicoder01/Customer_Segmentation.git
cd Customer_Segmentation

2. Install Required Libraries

pip install -r requirements.txt

3. Run Backend Server

python backend/app.py

Server will start at:

http://127.0.0.1:5000

4. Open Dashboard

Open your browser and go to:

http://127.0.0.1:5000

Note: Do not open index.html directly or via Live Server — always access the dashboard through the Flask server URL above.

Real World Applications

| Application              | Benefit                        |
|--------------------------|--------------------------------|
| Targeted Marketing       | Personalized campaigns         |
| Customer Insights        | Understand customer behavior   |
| Product Recommendations  | Suggest relevant products      |
| Marketing Optimization   | Better ROI                     |
| Business Strategy        | Data-driven decisions          |

Future Improvements

  • Add customer demographic segmentation (Age, Gender)
  • Improve visualization with Plotly
  • Add real-time analytics dashboard
  • Add model retraining feature with new data

Author

Ravi B.Tech Student | Machine Learning Enthusiast

GitHub