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Customer Segmentation Project

A smart and interactive web application for performing Customer Segmentation using KMeans clustering.
Built with Streamlit, this app allows users to upload their own customer dataset, analyze clusters dynamically, and explore rich insights through visual dashboards.

🚀 Features

  • 🔍 Upload your own dataset with custom mapping (coming soon)
  • 📊 Visualize and filter cluster segments in real-time
  • 📈 Radar chart of average metrics per segment
  • 📉 Compare features with interactive scatter plots
  • 📥 Bulk CSV upload and downloadable predictions
  • 🧠 AI-driven dynamic interpretation of each segment

📁 Expected Columns

Your uploaded dataset should contain the following columns:

Age, Annual_Income, Spending_Score, Purchase_Frequency,
Total_Spending, Family_Size, Gender, Marital_Status

If the dataset structure differs, upcoming versions will allow column mapping or retraining.

🧪 How It Works

  1. Upload a customer dataset CSV.
  2. The app preprocesses the data and predicts a segment using a trained KMeans model.
  3. Explore visual insights like:
    • Cluster distribution
    • Feature trends
    • Segment explanations

📦 Tech Stack

  • Python
  • Pandas, Scikit-learn, Matplotlib, Seaborn
  • Streamlit for UI
  • Joblib for model persistence

👨‍💻 Authors

  • Devendra Gurav
  • Pankaj Bhandari

🏁 Run Locally

git clone https://github.com/devendra011396/cust_segmentation.git
cd customer-segmentation
pip install -r requirements.txt
streamlit run model/streamlit_app.py

📫 For suggestions or improvements, feel free to reach out or fork the repo!