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➤ Overview
I engineered a high-impact Machine Learning solution that enables businesses to move beyond "one-size-fits-all" marketing. By leveraging advanced clustering and predictive modeling, this application identifies distinct customer personas and predicts future behavior to optimize retention and sales strategies.
➤ The Problem & Solution
Many businesses struggle to identify who their most valuable customers are and why they leave. I built this Streamlit powered dashboard to automate the entire data science pipeline from cleaning raw data to generating interactive segments allowing stakeholders to make data-driven decisions in seconds.
➤ Key Deliverables & Capabilities
◆ Behavioral Segmentation: Uses unsupervised learning to group customers by purchasing patterns, demographics, and engagement levels.
◆ Churn Prediction Engine: Integrated XGBoost model to predict which customers are likely to stop using a service, allowing for proactive retention.
◆ Interactive BI Dashboard: A user-friendly Streamlit interface that turns complex data into clear, visual charts and heatmaps.
◆ Actionable Data Export: Direct integration for downloading segmented lists into CSV/Excel for immediate use in email marketing or CRM tools.
➤ Tech Stack
◆ Machine Learning: Clustring K-Means, Scikit-learn (T-SNE/ SGD)
◆ Data Science: Python, Pandas, NumPy
◆ Visualization: Plotly, Seaborn
◆ Deployment: Streamlit Cloud / GitHub
➤ Business Value
◆ This tool is designed for E-commerce, SaaS, and Retail brands looking to:
◆ Increase Customer Lifetime Value (CLV) by targeting the right segments.
◆ Reduce Churn Rates through early-warning predictive signals.
◆ Personalize marketing campaigns for higher conversion rates.
➤ 🔗 View the Source Code on GitHub: [https://github.com/Usman-Waris/customer-intelligence-app ]
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Your ML model is 95% accurate. It's also useless. Here's why 👇
Built a model. Got 95% accuracy. Celebrated.
Then checked the confusion matrix.
Out of 1000 predictions:
950 were class 0 (majority)
50 were class 1 (the ones that actually mattered)
The model learned one thing: predict class 0. Always.
95% accurate. 0% useful.
This is the accuracy trap. And it kills real ML projects.
The fix? Stop looking at accuracy. Start looking at:
F1 Score: balances precision and recall
ROC-AUC: how well your model separates classes
Confusion Matrix: shows exactly where it fails
I ran into this while building a fraud detection pipeline. Switching metrics changed everything.
Accuracy is a lie when your data is imbalanced.
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