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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 What I built Developed a Natural
What I built Developed a Natural Language Processing (NLP) solution to detect DI Flag issues by analyzing and classifying text descriptions. How it works Cleaned text data by removing hashtags, HTML tags, special characters, and numeric values. Applied text preprocessing techniques including tokenization, stop-word removal, stemming, and lemmatization. Converted text into numerical features using Bag of Words (BoW) and TF-IDF. Trained and evaluated the model using accuracy and confusion matrix metrics. Performed 30 days of validation testing before production deployment. Technologies Python · NLP · Scikit-learn · TF-IDF · Bag of Words · Pandas · NumPy Outcome Built a text classification solution that helped identify DI Flag issues from descriptions and validated the model's performance before deployment.
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