Mastering Novelty Detection Using LOF in Python (Scikit-Learn)

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Want to detect outliers or rare events in real-time data streams? In this tutorial, you’ll learn how to perform novelty detection using the Local Outlier Factor (LOF) algorithm in Python with Scikit-Learn—perfect for fraud detection, monitoring, and anomaly detection systems. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Scikit-Learn and Machine Learning Playlist:    • Scikit-Learn Tutorials - Master Machine Le...   Local Outlier Factor:    • Mastering Outlier Detection with LOF (Loca...   Isolation Forest:    • Mastering Isolation Forest in Python: Anom...   Lasso Regression    • Mastering Ridge Regression in Python with ...   In this video, I show you how to use Local Outlier Factor (LOF) for novelty detection in production systems where you need to predict anomalies on new data in real time. This is a direct follow-up to my previous LOF video, so I highly recommend watching that first to understand the algorithm and why standard LOF only supports fit_predict, not separate training and prediction. I walk through the key limitation of standard LOF—it cannot train on historical data and then predict on new incoming records—and demonstrate exactly how to solve this by switching to novelty detection mode. The solution involves just two simple changes: setting novelty=True when initializing the model and using fit() instead of fit_predict(). This unlocks the predict() function, allowing you to train LOF on your historical dataset and then classify new data points as inliers or outliers as they arrive. I use a practical example with user query data that includes multiple features like query length and noun counts, showing how this approach works with multi-dimensional data just like you would encounter in real production analytics systems. By the end of the video, you will understand the difference between outlier detection and novelty detection, and you will be able to implement LOF in production environments for real-time anomaly tracking. TIMESTAMPS 00:00 Introduction & Prerequisites 01:00 LOF Limitation: No Predict Function 02:05 Setting Up the Problem 03:13 Adding Multiple Features with NLP 04:00 Creating Train-Test Split 04:40 Enabling Novelty Detection Mode 06:02 Using Predict on New Data 07:20 Recap & Production Applications OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.