Transform customer data into actionable insights with 99.96% accuracy
K-Means clustering identifies 3 distinct customer groups with optimal K-selection using Elbow Method and Silhouette Analysis.
XGBoost regression achieves 99.96% accuracy (R² = 0.9996) in predicting customer lifetime value.
Identify at-risk customers 3 months in advance with 87%+ accuracy (F1-Score: 0.85+) for proactive intervention.
Generate personalized engagement actions for each customer with priority scoring and expected impact.
Streamlit-powered dashboard with real-time predictions, ROI calculator, and customer lookup.
Docker containerization, cloud deployment guides, and comprehensive documentation included.
37% of customer base
41% of customer base
26% of customer base
Achieved through AI-powered personalized recommendations and segment-specific engagement strategies.
Optimized advertising spend with data-driven budget allocation across customer segments.
Reliable customer value forecasting enables confident business decisions and planning.
Proactive intervention replaces reactive responses, saving $800K+ in at-risk revenue.
Explore the complete codebase, documentation, and interactive demos