Artificial Intelligence: Evaluating AI, optimizing AI
Clustering validation with ROC Curves
This project builds a robust binary classification system to detect phishing websites using URL features like SSL state, web traffic, and domain patterns. Achieving 97% accuracy, 96.8% precision, and 0.9953 ROC-AUC, it ensures reliable, efficient detection with interpretable insights.
This repository contains many jupyter notebooks and output printouts chronicling my learning of basic Machine Learning concepts.
My DATAML200 - Pattern Recognition And Machine Learning course implementations.
Shiny App to help you generate adjusted ROC curves
Time to event analysis for neural data and motor units used in applied Brain machine interface (BMI)
Comprehensive ML case study predicting cardiovascular disease using UCI dataset. Implemented Logistic Regression (87% accuracy) & KNN (89% AUC) with advanced feature engineering, correlation analysis & clinical insights for healthcare decision support systems.
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