🛒 Customer Segmentation using K-Means

This project demonstrates customer segmentation using the K-Means clustering algorithm.
It groups customers based on Age, Annual Income, and Spending Score, helping businesses identify distinct customer groups for targeted marketing.

The project includes:
✅ A Jupyter Notebook for training & experimenting with clustering
✅ A Streamlit Web App for interactive visualization of customer segments

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🚀 Features

• Upload customer data (Mall_Customers.csv)
• Apply K-Means Clustering
• Automatically assign cluster names (e.g., High Spenders, Budget Shoppers)
• Interactive scatter plots with Plotly
• Choose number of clusters (k) dynamically from sidebar

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🛠️ Tech Stack

• Python 3.9+
• scikit-learn (ML model)
• pandas, numpy (data handling)
• plotly (interactive visualization)
• streamlit (frontend app)

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📂 Project Structure

│── data/
│   └── Mall_Customers.csv
│── notebooks/
│   └── customer_segmentation.ipynb
│── app.py
│── requirements.txt
│── .gitignore
│── README.md

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⚙️ Installation & Setup

1. Clone the repo

   git clone https://github.com/ashfaq3112/Customer_Segmentation.git
   cd Customer_Segmentation

2.**Create virtual environment (optional but recommended) **

python -m venv venv
source venv/bin/activate   # On Mac/Linux
venv\Scripts\activate      # On Windows

3.Install dependencies

pip install -r requirements.txt   

4.*Run Streamlit app

streamlit run app.py

📌 Example Use Cases

🛍️ Retail stores → Identify premium vs budget customers

💳 Banks/Finance → Segment credit card holders based on spending behavior

📈 Marketing Teams → Create targeted campaigns