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Analysed comments across social platforms using NLP, K-Means clustering, and VADER to classify sentiment at scale with 92% positive signal accuracy.
Built a real-time sentiment pipeline on live Twitter/X data using the v2 API with custom preprocessing, topic clustering, and trend detection.
End-to-end churn prediction using XGBoost and SHAP to identify at-risk fintech users — drove a 22% reduction in 90-day churn and ~₹8Cr in retained revenue.
Built a Bayesian MMM with adstock and saturation to measure true marketing ROI across 6 channels — improved blended efficiency by 31% and freed ₹3Cr in reallocated spend.
Monte Carlo simulation engine that models 36-month revenue under uncertainty — surfaced churn as 2× the lever of acquisition, reshaping ₹30Cr+ annual planning.
Real-time anomaly detection system for payment success rates, transaction volumes, and latency — reduced mean time to detect critical payment issues by 70%.