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recall-optimization

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FaezehFarhan / Dyslipidemia

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Dyslipidemia, a condition with abnormal lipid levels in the blood, significantly increases the risk of cardiovascular diseases like heart attacks and strokes. This project aims to build accurate models for predicting dyslipidemia using both machine learning (ML) and deep learning (DL) techniques. The primary focus is on maximizing recall to minimiz

data-science machine-learning deep-learning neural-networks xgboost predictive-modeling smote medical-ai medical-diagnosis false-negatives healthcare-ai dyslipidemia-prediction dyslipidemia cardiovascular-diseases-prediction recall-optimization
  • Updated Oct 9, 2024
  • Jupyter Notebook

SuryaVamsi-P / Credit-Card-Fraud-Detection-ML

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Built a machine learning pipeline to detect fraudulent credit card transactions using real-world data (1M+ rows). Explored fraud patterns, validated key hypotheses, and optimized models like KNN, Random Forest, and SVM with 98%+ recall. Strong focus on data insights & impact.

python data-science machine-learning random-forest svm scikit-learn eda classification data-analysis feature-engineering financial-data knn binary-classification imbalanced-data model-evaluation fraud-detection credit-card-transactions recall-optimization fraudulent-activity real-world-dataset
  • Updated May 24, 2025
  • Python

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