Freelancers using labelimg

Freelancers using labelimgFreelancers using labelimg

Cover image for Context-Aware Fintech User Feedback Classification

Context-Aware Fintech User Feedback Classification & Urgency Detection Most feedback tools stop at sentiment; positive or negative. This project goes further by building a decision-driven NLP system that classifies fintech user feedback by issue type and detects operational urgency, enabling smarter triage and escalation. What was built: Two machine learning models trained on manually collected and annotated app store reviews from real fintech platforms like Revolut, Wise, and Monzo: Issue Classifier: categorizes feedback into 9 classes including transaction issues, account security, KYC, refunds, app performance, and fraud Urgency Detector: binary classifier that flags high-risk feedback requiring immediate attention, optimized for recall to minimize missed critical cases

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Cover image for Annotated computer vision datasets used

Annotated computer vision datasets used for vehicle detection and Automatic Number Plate Recognition (ANPR) systems. The project involved labeling vehicles and number plates in traffic camera footage to help train AI models used in smart city infrastructure, traffic monitoring, and law enforcement systems. Key responsibilities included: • Vehicle detection using bounding box annotation • Number plate annotation for ANPR systems • Dataset preparation for computer vision models • Annotation quality validation and dataset review These datasets support AI models used in traffic monitoring systems, automated toll collection, smart parking systems, and urban mobility analytics.

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Cover image for Text annotation

Text annotation

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