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lime-explainability

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ascender1729 / HealthFraudMLChain

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M.Sc. Dissertation: Healthcare insurance fraud detection using Optuna-tuned ML ensemble (F1=0.7345) with SHA-256 blockchain audit trails and ECIES encryption. 5,410 providers, 190 features, SHAP/LIME explainability.

machine-learning thesis nextjs blockchain xgboost lightgbm ensemble-learning sha256 merkle-tree fraud-detection catboost shap optuna ecies-encryption nit-patna healthcare-fraud-detection lime-explainability
  • Updated Apr 9, 2026
  • Python

Himanshu-Laddhad / Customer-review-intelligence-nlp

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3-stage NLP pipeline on 568k Amazon reviews — VADER baseline → TF-IDF + LinearSVC → DistilBERT fine-tuning. Adds Aspect-Based Sentiment Analysis (Quality/Price/Delivery/Service) and LIME explainability. Surfaces star-rating disconnects that predict return rates.

python nlp machine-learning deep-learning sentiment-analysis text-classification transformers spacy tfidf aspect-based-sentiment-analysis huggingface streamlit distilbert retail-analytics lime-explainability
  • Updated May 24, 2026
  • Jupyter Notebook

bk1210 / Detecting-Sarcasm-as-Sentiment-Incongruence

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Multi-task RoBERTa for sarcasm detection via Sentiment Incongruence Auto-Labeling. Focal Loss + WeightedRandomSampler + LIME explainability. F1-Macro 0.977, AUC 0.997. Zero-shot cross-domain eval on ABSA & Amazon. IEEE paper.

nlp data-science natural-language-processing deep-learning sentiment-analysis text-classification transformers pytorch data-analytics cross-domain multi-task-learning sarcasm-detection focal-loss roberta huggingface lime-explainability implicit-sentiment
  • Updated Apr 22, 2026
  • Jupyter Notebook

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