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Problem: Financial institution needed to detect fraudulent transactions in real-time with minimal false positives.

Solution: Developed ensemble model (Random Forest + Neural Network) processing transactions in under 100ms. Implemented feature engineering pipeline with 47 derived features including velocity checks and behavioral patterns.

Outcome: Achieved 94.2% precision and 89.7% recall. Prevented $2.3M in fraud losses over 6-month period. Reduced manual review queue by 68%.

Problem: E-commerce company needed to predict customer lifetime value for targeted marketing and resource allocation.

Solution: Built gradient boosting model using 3 years of transaction history, behavioral data, and demographic features. Implemented time-series cross-validation to prevent data leakage.

Outcome: Model RMSE of $127 on CLV predictions (avg CLV $2,400). Marketing ROI increased by 34% through precise customer segmentation. Deployed via REST API serving 1,000+ predictions/hour.

Problem: Retail chain struggled with inventory management, leading to stockouts and excess inventory costs.

Solution: Developed time-series forecasting models using Prophet and SARIMA for 500+ SKUs across 20 locations. Incorporated holiday effects, promotions, and seasonality. Built interactive Tableau dashboard for operations team.

Outcome: Forecasting MAPE improved from 23% to 11%. Reduced stockouts by 45% and excess inventory by 28%, saving $600K annually. Dashboard adopted by 30+ users company-wide.

Problem: Brand needed to analyze customer sentiment from 100K+ monthly reviews and social media mentions at scale.

Solution: Fine-tuned BERT model on domain-specific text data. Built scalable pipeline using Docker and AWS Lambda for batch processing. Implemented aspect-based sentiment analysis for product features.

Outcome: Achieved 87% accuracy on sentiment classification. Processed 3M+ text samples in first quarter. Insights led to product improvements addressing top 5 customer pain points.