🧠 Customer Segmentation App (K-Means Clustering)

An interactive Machine Learning web application that segments customers into distinct groups based on income and spending behavior using K-Means clustering.

🌐 Live App: https://customer-segementation-app-ek2xidbyggeejmyam2k9ed.streamlit.app/

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🚀 Project Overview

This project uses unsupervised machine learning to identify customer segments for targeted marketing and business decision-making.

The application allows users to input customer details and instantly see which segment they belong to.

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🎯 Key Features

✅ Interactive web interface built with Streamlit
✅ Real-time customer segmentation
✅ Pre-trained K-Means clustering model
✅ Data preprocessing with StandardScaler
✅ Clean and simple UI
✅ Instant prediction output

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🧮 Machine Learning Model

• Algorithm: K-Means Clustering
• Model Type: Unsupervised Learning
• Preprocessing: Standard Scaling
• Libraries: Scikit-learn, Joblib

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📊 Input Features

• Annual Income
• Spending Score
• Other behavioral attributes (if applicable)

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🛠️ Tech Stack

Programming Language: Python 🐍

Libraries & Tools:

• Streamlit
• Pandas
• NumPy
• Scikit-learn
• Joblib
• OpenPyXL

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📁 Project Structure

customer-segmentation-app/ │ ├── app.py # Streamlit application ├── kmeans_model.pkl # Trained ML model ├── scaler.pkl # Data scaler ├── model_features.pkl # Feature list ├── requirements.txt # Dependencies └── README.md

🌐 Deployment

This app is deployed using Streamlit Community Cloud.

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💼 Business Use Case

Customer segmentation helps organizations:

• Identify high-value customers
• Personalize marketing strategies
• Improve customer retention
• Optimize product offerings

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▶️ Run Locally

1. Clone the repository: https://github.com/ManvithaReddy1133/customer-segementation-app
2. Navigate to project folder::
3. Install dependencies:
4. Run the app:
5. Deployment

This app is deployed using Streamlit Community Cloud.

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💼 Business Use Case

Customer segmentation helps organizations:

• Identify high-value customers
• Personalize marketing strategies
• Improve customer retention
• Optimize product offerings