Insider Threats: Anomaly Detection on User Logs
Unsupervised ML flags suspicious behavior (USB events, file access) to surface potential insider risks early.
Explore real-world data science & machine learning work. Each post breaks down the approach and takeaways — and how the same methods can uncover opportunities, reduce risk, and guide better decisions.
Unsupervised ML flags suspicious behavior (USB events, file access) to surface potential insider risks early.
Training a convolutional model to classify emotions from 2D images — data prep, architecture, and results.
Binary classification on histopathologic scans; pipeline, metrics, and a clean Kaggle-ready submission.
Supervised learning over MITRE-labeled network data to highlight command-and-control patterns.
Stepwise regression linking environmental and genetic factors (5-HTT) with depression outcomes.
Feature engineering from pitch-level data to predict run-risk innings with ~83% accuracy.
Training a CycleGAN to translate real-world images to Monet-style outputs; data, training, and MiFID.
Transformer-based classification for disaster vs. non-disaster tweets with minimal preprocessing.
Dimensionality reduction + supervised/unsupervised models to categorize news at scale.
From feature selection to evaluation — a practical model to filter low-quality job listings.
Finding revenue-maximizing price points by product with historical demand and price variation.
Transformations, lack-of-fit testing, and why the power model won — a compact walkthrough.