{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Use Nearest Neighbors for Anomaly Detection","slug":"how-data-scientists-use-nearest-neighbors-for-anomaly-detection","published_at":"2026-07-07T08:49:58+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-nearest-neighbors-for-anomaly-detection","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0095.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0095.mp3","summary":"In Episode 95 of The Data Science Podcast with Fexingo, Lucas and Luna dive into a practical yet underappreciated technique: using k-nearest neighbors for anomaly detection. They kick off with a real-world story from a major credit card processor that flagged a series of fraudulent transactions by measuring distance to the nearest legitimate patterns. Lucas explains why distance-based methods can outperform deep learning in low-signal, high-stakes settings, especially when you need interpretable reasons for each flag. Luna challenges him on scalability and the curse of dimensionality, and they discuss how companies like Stripe and PayPal have used variants of k-NN in production fraud pipelines. They also touch on the trade-offs between global and local outlier factors, and how to choose k when the definition of 'normal' shifts over time. A concrete segment on choosing distance metrics — Euclidean vs. Manhattan vs. cosine — gives listeners an actionable guideline. Mid-episode, they weave in a natural request for listener support, tying it back to the value of open-source tools. If you've ever wondered when to reach for a simple nearest-neighbor approach instead of a neural network, this episode gives you the framework. #DataScience #MachineLearning #AnomalyDetection #KNearestNeighbors #FraudDetection #OutlierDetection #DistanceMetrics #LocalOutlierFactor #Stripe #PayPal #CurseOfDimensionality #Interpretability #Technology #FexingoBusiness #BusinessPodcast #TechPodcast #DataSciencePodcast #ProductionML Keep every episode free: buymeacoffee.com/fexingo","meta_description":"In Episode 95 of The Data Science Podcast with Fexingo, Lucas and Luna dive into a practical yet underappreciated technique: using k-nearest neighbors for…","key_points":[],"chapters":[],"topics":[],"duration_seconds":540,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-nearest-neighbors-for-anomaly-detection/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-nearest-neighbors-for-anomaly-detection.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}