📊 Customer Segmentation Using Machine Learning (K-Means Clustering)

Learn how to perform customer segmentation using machine learning in this complete Python data science project. In this project, we walk through the full workflow—from raw data to deployed web application—showing how businesses can identify meaningful customer groups and drive data-driven decisions.

We use K-Means clustering to segment customers based on demographic and behavioral features, helping uncover hidden patterns in customer data.

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🚀 What You’ll Learn

• Data preprocessing and feature selection
• Customer segmentation using K-Means clustering
• Feature scaling with StandardScaler
• Model training and evaluation using Scikit-learn
• Building an interactive Streamlit dashboard
• Deploying a machine learning app live on Streamlit Cloud

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🌐 Live Application

👉 Try the live app here: 🔗 https://customer-segmentation-using-ml.streamlit.app/

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📸 Dashboard Preview

Here’s a screenshot of the interactive Streamlit dashboard:

🛠 Tech Stack

• Python
• Pandas & NumPy
• Scikit-learn
• K-Means Clustering
• Streamlit
• Joblib

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💡 Why Customer Segmentation Matters

Customer segmentation helps businesses:

• Identify high-value customers
• Personalize marketing strategies
• Improve customer retention
• Increase revenue through targeted actions

This project demonstrates how machine learning transforms raw customer data into actionable business insights.