Mastering Outlier Detection with LOF (Local Outlier Factor) in Python

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Looking for a smarter way to detect outliers in your data? In this tutorial, you’ll learn how to use Local Outlier Factor (LOF) from Scikit-Learn to find anomalies based on local density—perfect for fraud detection, network intrusion, and any dataset where context matters!

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In this video, I break down the Local Outlier Factor (LOF) algorithm and show you how to use it for anomaly detection in real-world data. LOF is an unsupervised machine learning algorithm that identifies outliers by measuring the local density deviation of data points compared to their neighbors, making it incredibly effective for detecting anomalies in clustered datasets.

We walk through the core concepts behind LOF, including how it calculates K-distances, local reachability density, and anomaly scores for each data point. I explain why LOF excels at handling datasets with varying cluster densities and compare its performance against other popular anomaly detection algorithms like Isolation Forest and One-Class SVM.

Using a practical example with search query data, I demonstrate how to implement LOF in Python with scikit-learn, including how to choose the right number of neighbors and contamination parameters. We analyze query length and noun count metrics to identify unusual user behavior patterns, and I show you how to visualize the results to understand which data points are flagged as anomalies. By the end of this tutorial, you'll know exactly when to use LOF and how to apply it to your own anomaly detection projects.

TIMESTAMPS
00:00 Introduction & Discord Community
00:50 What is Local Outlier Factor (LOF)?
02:07 How LOF Works - Local Density Deviation
03:05 K-Distance Calculation Explained
04:25 Local Reachability Distance (LRD)
05:13 Determining Inliers vs Outliers
05:55 Visual Example of LOF
07:30 Understanding Cluster Effects on Outlier Scores
09:40 Comparing LOF to Other Algorithms
12:20 Code Implementation - Loading Data
14:00 Adding Noun Count Feature with Spacy
15:40 Choosing Number of Neighbors Parameter
19:20 Contamination Parameter Explained
20:40 Fitting the Model & Predictions
22:00 Visualizing Results
24:30 Analyzing Output & Limitations

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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.

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