🛍️ E-commerce User Behavior Analysis Dashboard

Welcome to the E-commerce User Behavior Analysis Dashboard, a powerful data analytics and customer segmentation tool built using Python, Pandas, Scikit-learn, and Streamlit. It transforms raw user behavior data into actionable insights for marketing strategy, business growth, and retention.

🎯 Project Objectives

• Analyze user behavior (view, cart, buy, etc.) on an e-commerce platform.
• Perform RFM Analysis to segment users based on Recency, Frequency, and Monetary value.
• Use K-Means Clustering to group users into meaningful segments.
• Visualize user segments and trends with an interactive dashboard.
• Suggest targeted marketing strategies per customer segment.
• Allow business stakeholders to download segmented reports for operational use.

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🧰 Technologies & Libraries Used

• Python 3
• Pandas, NumPy – Data manipulation
• Matplotlib, Seaborn – Visualizations
• Scikit-learn – Clustering & Scaling
• Streamlit – Interactive dashboard UI
• Colorama, IPython.display – Aesthetic enhancements
• CSV – Data download & export

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📊 Features

🔍 Behavior Filter

• Select specific behavior types (e.g. view, cart, buy) from the sidebar.

🎨 Theme Customization

• Switch between Light and Dark modes.

📈 Dashboard Visualizations

• Raw dataset viewer
• RFM Histograms
• Clustering boxplots
• Cluster distribution pie chart
• Segment distribution bar plot
• Heatmap and table summary of RFM by segment
• Daily & Monthly time series purchase trends

💾 Downloads

• Download clustered user data (rfm_segmented.csv)
• Download marketing strategies per segment (marketing_strategies.csv)

💡 Precision Marketing Suggestions

Segment	Description	Strategy
VIP	High frequency, high spend, recent buyers	Loyalty rewards, early access
Loyal	Repeat customers, moderate spenders	Upsell/cross-sell
Churn Risk	Previously active, now disengaged	Win-back campaigns
At_Risk	Infrequent, low spenders	Retargeting, discounts

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📁 Project Structure

📦 E-commerce-User-Behavior-Analysis
├── 📊 analysis_notebook.ipynb        # Core data analysis & clustering logic
├── 📈 dashboard_app.py               # Streamlit-based dashboard (uploaded)
├── 📄 UserBehavior.csv               # Dataset (100,000 sampled rows)
└── README.md                         # You’re here!

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🚀 How to Run the Project

⚠️ Make sure you have Python 3.8+ and pip installed.

1. Install Requirements

pip install streamlit pandas numpy matplotlib seaborn scikit-learn

2. Run the Dashboard

streamlit run dashboard_app.py

The dashboard will open in your browser at http://localhost:8501.

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🧠 Sample Use Cases

• 👩‍💼 Marketing Teams – Discover user segments and tailor campaigns
• 🧑‍💻 Product Managers – Understand engagement trends
• 📈 Data Analysts – Run deeper behavioral segmentation experiments

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🛠 Future Improvements

• ✅ Add login authentication to secure dashboard access
• 📦 Deploy on the web (e.g. Streamlit Cloud or Heroku)
• 🔍 Include more advanced clustering (e.g. DBSCAN, Hierarchical)
• 🧠 Add machine learning to predict customer churn

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

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📄 License

This project is open-source and available under the MIT License.

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📬 Contact

Feel free to connect with me:

• GitHub: SecureAuditX
• LinkedIn: (your link here)
• Email: (abdulkarimumar86@gmail.com)