{"id":4738,"date":"2023-09-04T05:03:11","date_gmt":"2023-09-04T05:03:11","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=4738"},"modified":"2024-07-10T11:41:35","modified_gmt":"2024-07-10T11:41:35","slug":"anomaly-detection-in-machine-learning","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/","title":{"rendered":"Anomaly detection Machine Learning algorithms"},"content":{"rendered":"<p><b>Summary: <\/b><span style=\"font-weight: 400;\">Data can be deceiving. Sometimes, the most valuable insights lie in the unexpected \u2013 the anomalies. Anomaly detection, a powerful machine learning technique, helps us spot these outliers, the data points that deviate significantly from the norm.\u00a0 This blog explores various anomaly detection algorithms, from statistical methods to complex AI techniques. <\/span><b>\u00a0<\/b><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Introduction\" >Introduction\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Anomaly_Detection_in_Machine_Learning\" >Anomaly Detection in Machine Learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Anomaly_Detection_Machine_Learning_Example\" >Anomaly Detection Machine Learning Example<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Network_Intrusion_Detection\" >Network Intrusion Detection<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Healthcare_Monitoring\" >Healthcare Monitoring<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Manufacturing_Quality_Control\" >Manufacturing Quality Control\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Energy_Usage_Monitoring\" >Energy Usage Monitoring<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Fraud_Detection_in_Financial_Transactions\" >Fraud Detection in Financial Transactions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Aircraft_Engine_Performance_Monitoring\" >Aircraft Engine Performance Monitoring<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#E-commerce_Customer_Behaviour\" >E-commerce Customer Behaviour<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Environmental_Monitoring\" >Environmental Monitoring<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Supply_Chain_Anomalies\" >Supply Chain Anomalies<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Server_Log_Analysis\" >Server Log Analysis<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Anomaly_Detection_Machine_Learning_Techniques\" >Anomaly Detection Machine Learning Techniques<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Unsupervised_Anomaly_Detection\" >Unsupervised Anomaly Detection<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Artificial_Neural_Networks_ANNs\" >Artificial Neural Networks (ANNs)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Density-Based_Spatial_Clustering_of_Applications_with_Noise_DBSCAN\" >Density-Based Spatial Clustering of Applications with Noise (DBSCAN)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Isolation_Forest\" >Isolation Forest<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Gaussian_Mixture_Models_GMM\" >Gaussian Mixture Models (GMM)\u00a0<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Supervised_Anomaly_Detection\" >Supervised Anomaly Detection<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Support_Vector_Machines_SVM\" >Support Vector Machines (SVM)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Random_Forests\" >Random Forests<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#k-Nearest_Neighbors_k-NN\" >k-Nearest Neighbors (k-NN)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Semi-Supervised_Anomaly_Detection\" >Semi-Supervised Anomaly Detection<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Pre-trained_Models\" >Pre-trained Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Transfer_Learning\" >Transfer Learning<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#How_to_do_Anomaly_Detection_using_Machine_Learning_in_Python\" >How to do Anomaly Detection using Machine Learning in Python?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#What_are_the_Different_Types_of_Anomaly_Detection_Algorithms\" >What are the Different Types of Anomaly Detection Algorithms?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#How_Do_I_Choose_the_Right_Anomaly_Detection_Algorithm\" >How Do I Choose the Right Anomaly Detection Algorithm?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.pickl.ai\/blog\/anomaly-detection-in-machine-learning\/#Is_Anomaly_Detection_Foolproof\" >Is Anomaly Detection Foolproof?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Anomaly detection is identified as one of the most common use cases in <\/span><a href=\"https:\/\/pickl.ai\/blog\/eager-learning-and-lazy-learning-in-machine-learning-a-comprehensive-comparison\/\"><span style=\"font-weight: 400;\">Machine Learning<\/span><\/a><span style=\"font-weight: 400;\">. The purpose of finding and identifying outliers is helpful in prevention of fraudulent activities, adversary attacks and network intrusions that have the ability to compromise the company\u2019s future.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Further, it will provide a step-by-step guide on anomaly detection Machine Learning python.\u00a0<\/span><\/p>\n<h2 id=\"anomaly-detection-in-machine-learning\"><span class=\"ez-toc-section\" id=\"Anomaly_Detection_in_Machine_Learning\"><\/span><b>Anomaly Detection in Machine Learning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">An approach to data analysis and Machine Learning called \u201canomaly detection,\u201d also referred to as \u201coutlier detection,\u201d focuses on finding data points or patterns that considerably differ from what is considered to be \u201cnormal\u201d or anticipated behaviour.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Observations that deviate from the majority of the data are known as anomalies and might take the shape of occurrences, trends, or events that differ from customary or expected behaviour.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Finding anomalous occurrences that might point to intriguing or potentially significant events is the aim of anomaly detection. Anomalies could be a sign of many different things, including fraud, mistakes, flaws, health problems, security breaches, and more. In many fields, finding anomalies can yield insightful data and useful information.<\/span><\/p>\n<h2 id=\"anomaly-detection-machine-learning-example\"><span class=\"ez-toc-section\" id=\"Anomaly_Detection_Machine_Learning_Example\"><\/span><b>Anomaly Detection Machine Learning Example<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In this section, we will highlight anomaly detection, a powerful machine learning technique that can identify these outliers. Anomaly detection unlocks a world of possibilities, and given below are the <\/span><a href=\"https:\/\/pickl.ai\/blog\/a-step-by-step-complete-guide-to-principal-component-analysis-pca-for-beginners\/\"><span style=\"font-weight: 400;\">Machine Learning<\/span><\/a><span style=\"font-weight: 400;\"> anomaly detection examples that you need to know about:\u00a0<\/span><\/p>\n<h3 id=\"network-intrusion-detection\"><span class=\"ez-toc-section\" id=\"Network_Intrusion_Detection\"><\/span><b>Network Intrusion Detection<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection <\/span><a href=\"https:\/\/pickl.ai\/blog\/machine-learning-algorithms-that-every-ml-engineer-should-know\/\"><span style=\"font-weight: 400;\">Machine Learning algorithms<\/span><\/a><span style=\"font-weight: 400;\"> is used to monitor network traffic and identify unusual patterns that might indicate a cyberattack or unauthorised access. For instance, sudden spikes in data traffic or unusual communication patterns between devices can be flagged as anomalies.<\/span><\/p>\n<h3 id=\"healthcare-monitoring\"><span class=\"ez-toc-section\" id=\"Healthcare_Monitoring\"><\/span><b>Healthcare Monitoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection can be applied to patient monitoring data to identify irregularities in vital signs. This could include detecting unusual heart rhythms in ECG data or unexpected variations in blood pressure that might indicate a health issue.<\/span><\/p>\n<h3 id=\"manufacturing-quality-control\"><span class=\"ez-toc-section\" id=\"Manufacturing_Quality_Control\"><\/span><b>Manufacturing Quality Control\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection can be used to monitor the output of manufacturing processes. It can identify faulty products by analysing sensor data, such as detecting defects in the shape or size of products on an assembly line.<\/span><\/p>\n<h3 id=\"energy-usage-monitoring\"><span class=\"ez-toc-section\" id=\"Energy_Usage_Monitoring\"><\/span><b>Energy Usage Monitoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection is used to identify abnormal energy consumption patterns in industrial or residential settings. Sudden spikes or drops in energy usage can indicate equipment malfunction or energy theft.<\/span><\/p>\n<h3 id=\"fraud-detection-in-financial-transactions\"><span class=\"ez-toc-section\" id=\"Fraud_Detection_in_Financial_Transactions\"><\/span><b>Fraud Detection in Financial Transactions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection techniques are used to identify fraudulent credit card transactions. Transactions that deviate from a user\u2019s usual spending patterns or involve unusual locations can be flagged for further investigation.<\/span><\/p>\n<h3 id=\"aircraft-engine-performance-monitoring\"><span class=\"ez-toc-section\" id=\"Aircraft_Engine_Performance_Monitoring\"><\/span><b>Aircraft Engine Performance Monitoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection is used to monitor aircraft engine health. By analysing data from various sensors on the engine, deviations from normal operating conditions can be detected, allowing maintenance crews to address potential issues before they lead to failures.<\/span><\/p>\n<h3 id=\"e-commerce-customer-behaviour\"><span class=\"ez-toc-section\" id=\"E-commerce_Customer_Behaviour\"><\/span><b>E-commerce Customer Behaviour<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection can be used to identify unusual patterns in customer behaviour on e-commerce platforms. For instance, sudden changes in purchase habits or unusually high cart abandonment rates might indicate fraud or other issues.<\/span><\/p>\n<h3 id=\"environmental-monitoring\"><span class=\"ez-toc-section\" id=\"Environmental_Monitoring\"><\/span><b>Environmental Monitoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection is used to monitor environmental factors like air quality and water pollution. Unusual variations in pollutant levels or other environmental parameters can be indicative of an incident or pollution source.<\/span><\/p>\n<h3 id=\"supply-chain-anomalies\"><span class=\"ez-toc-section\" id=\"Supply_Chain_Anomalies\"><\/span><b>Supply Chain Anomalies<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection applied to supply chain data to identify disruptions or irregularities. Unexpected delays in shipping, drastic changes in order quantities, or sudden supplier changes can be flagged as anomalies.<\/span><\/p>\n<h3 id=\"server-log-analysis\"><span class=\"ez-toc-section\" id=\"Server_Log_Analysis\"><\/span><b>Server Log Analysis<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection used to monitor server logs and identify unusual patterns that might indicate a security breach or system failure. This could include sudden spikes in failed login attempts or unusual patterns of resource usage.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These examples highlight the versatility of anomaly detection in various domains. The specific techniques and algorithms used can vary based on the nature of the data and the problem at hand.<\/span><\/p>\n<h2 id=\"anomaly-detection-machine-learning-techniques\"><span class=\"ez-toc-section\" id=\"Anomaly_Detection_Machine_Learning_Techniques\"><\/span><b>Anomaly Detection Machine Learning Techniques<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Anomaly detection, a powerful branch of machine learning, equips us to identify these outliers \u2013 the data points that deviate significantly from the norm. In this section, we&#8217;ll explore various machine learning techniques for anomaly detection, from statistical methods to complex algorithms.<\/span><b>\u00a0<\/b><\/p>\n<h3 id=\"unsupervised-anomaly-detection\"><span class=\"ez-toc-section\" id=\"Unsupervised_Anomaly_Detection\"><\/span><b>Unsupervised Anomaly Detection<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Unsupervised anomaly detection tackles this challenge, empowering us to find hidden patterns and outliers in data, ultimately leading to better decision-making and improved security.<\/span><\/p>\n<h4 id=\"artificial-neural-networks-anns\"><span class=\"ez-toc-section\" id=\"Artificial_Neural_Networks_ANNs\"><\/span><b>Artificial Neural Networks (ANNs)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Autoencoders, a type of neural network, can used for unsupervised anomaly detection. An autoencoder consists of an encoder network that maps input data to a lower-dimensional representation, and a decoder network that reconstructs the input from the lower-dimensional representation.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">During training, the model learns to minimise the reconstruction error. Anomalies, being different from normal data, result in higher reconstruction errors.<\/span><\/p>\n<h4 id=\"density-based-spatial-clustering-of-applications-with-noise-dbscan\"><span class=\"ez-toc-section\" id=\"Density-Based_Spatial_Clustering_of_Applications_with_Noise_DBSCAN\"><\/span><b>Density-Based Spatial Clustering of Applications with Noise (DBSCAN)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">DBSCAN is a density-based clustering algorithm. It identifies regions of high data point density as clusters and flags points with low densities as anomalies. Points that don\u2019t belong to any cluster or in low-density regions considered anomalies.<\/span><\/p>\n<h4 id=\"isolation-forest\"><span class=\"ez-toc-section\" id=\"Isolation_Forest\"><\/span><b>Isolation Forest<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The Isolation Forest algorithm creates a random partition of the data by selecting features and random split values. Anomalies can isolated more quickly as they require fewer splits to\u00a0 separated from the majority of the data. This algorithm is efficient and effective for high-dimensional datasets.<\/span><\/p>\n<h4 id=\"gaussian-mixture-models-gmm\"><span class=\"ez-toc-section\" id=\"Gaussian_Mixture_Models_GMM\"><\/span><b>Gaussian Mixture Models (GMM)\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">GMM represents the data distribution as a mixture of several Gaussian distributions. Anomalies might have low probabilities under the fitted GMM, as they deviate from the common Gaussian patterns observed in normal data.<\/span><\/p>\n<h3 id=\"supervised-anomaly-detection\"><span class=\"ez-toc-section\" id=\"Supervised_Anomaly_Detection\"><\/span><b>Supervised Anomaly Detection<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Supervised anomaly detection is a method that leverages labelled data to train models for pinpointing anomalies. We will explore how supervised learning empowers us to fight fraud, detect equipment failures, and uncover other hidden abnormalities within our data sets.<\/span><\/p>\n<h4 id=\"support-vector-machines-svm\"><span class=\"ez-toc-section\" id=\"Support_Vector_Machines_SVM\"><\/span><b>Support Vector Machines (SVM)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">In a supervised context, SVM trained to find a hyperplane that best separates normal instances from anomalies. Anomalies treated as the minority class, and the model aims to maximise the margin between the two classes.<\/span><\/p>\n<h4 id=\"random-forests\"><span class=\"ez-toc-section\" id=\"Random_Forests\"><\/span><b>Random Forests<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Random Forests can adapted for anomaly detection by treating it as malous data. Instances that are difficult to classify (misclassified or those in the minority class) receive higher outlier scores, indicating they might be anomalies.<\/span><\/p>\n<h4 id=\"k-nearest-neighbors-k-nn\"><span class=\"ez-toc-section\" id=\"k-Nearest_Neighbors_k-NN\"><\/span><b>k-Nearest Neighbors (k-NN)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">In the supervised approach, <\/span><a href=\"https:\/\/pickl.ai\/blog\/unlocking-the-power-of-knn-algorithm-in-machine-learning\/\"><span style=\"font-weight: 400;\">k-NN<\/span><\/a><span style=\"font-weight: 400;\"> assigns labels to instances based on their k-nearest neighbours. Anomalies assigned to the class where they have the fewest neighours, or instances that are far from their neighbours can identified as anomalies.<\/span><\/p>\n<h3 id=\"semi-supervised-anomaly-detection\"><span class=\"ez-toc-section\" id=\"Semi-Supervised_Anomaly_Detection\"><\/span><b>Semi-Supervised Anomaly Detection<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Semi-supervised techniques leverage a combination of labelled normal data and unlabeled data to enhance anomaly detection performance. Choosing the right technique depends on the characteristics of your data, the distribution of anomalies, and the available resources for training and evaluation.\u00a0<\/span><\/p>\n<h4 id=\"pre-trained-models\"><span class=\"ez-toc-section\" id=\"Pre-trained_Models\"><\/span><b>Pre-trained Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Models that are pre-trained on a large dataset, like <\/span><a href=\"https:\/\/pickl.ai\/blog\/top-deep-learning-algorithms-in-machine-learning\/\"><span style=\"font-weight: 400;\">deep learning models<\/span><\/a><span style=\"font-weight: 400;\"> trained on ImageNet, can be fine-tuned for anomaly detection on a specific problem. Anomalies might lead to deviations from the normal patterns the model has learned.<\/span><\/p>\n<h4 id=\"transfer-learning\"><span class=\"ez-toc-section\" id=\"Transfer_Learning\"><\/span><b>Transfer Learning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Transfer learning involves using a pre-trained model from one domain to solve a related task in another domain. By fine-tuning the model with your data, you can leverage its learned features for anomaly detection.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s important to experiment and iterate to find the most effective approach for your specific use case.<\/span><\/p>\n<h2 id=\"how-to-do-anomaly-detection-using-machine-learning-in-python\"><span class=\"ez-toc-section\" id=\"How_to_do_Anomaly_Detection_using_Machine_Learning_in_Python\"><\/span><b>How to do Anomaly Detection using Machine Learning in Python?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Here\u2019s a detailed step-by-step guide on how to perform anomaly detection using Machine Learning in <\/span><a href=\"https:\/\/pickl.ai\/blog\/gigantic-python\/\"><span style=\"font-weight: 400;\">Python<\/span><\/a><span style=\"font-weight: 400;\">. We\u2019ll use a simple example of credit card fraud detection and the Isolation Forest algorithm for this demonstration.<\/span><\/p>\n<p><b>Step 1: <\/b><span style=\"font-weight: 400;\">Import Libraries Start by importing the necessary libraries.<\/span><\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-11418\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6.png\" alt=\"\" width=\"580\" height=\"123\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6.png 580w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-300x64.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-110x23.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-200x42.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-380x81.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-255x54.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-550x117.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image6-150x32.png 150w\" sizes=\"(max-width: 580px) 100vw, 580px\" \/><\/p>\n<p><b>Step 2<\/b><span style=\"font-weight: 400;\">: Load and Explore Data Load your dataset and explore its structure and content.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-11419\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5.png\" alt=\"\" width=\"589\" height=\"165\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5.png 589w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-300x84.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-110x31.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-200x56.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-380x106.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-255x71.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-550x154.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image5-150x42.png 150w\" sizes=\"(max-width: 589px) 100vw, 589px\" \/><\/p>\n<p><b>Step 3: <\/b><span style=\"font-weight: 400;\">Data Preprocessing Preprocess the data by handling missing values and scaling numerical features.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-11422\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3.png\" alt=\"\" width=\"582\" height=\"229\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3.png 582w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-300x118.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-110x43.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-200x79.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-380x150.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-255x100.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-550x216.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image3-150x59.png 150w\" sizes=\"(max-width: 582px) 100vw, 582px\" \/><\/p>\n<p><b>Step 4:<\/b><span style=\"font-weight: 400;\"> Model Training Train the Isolation Forest model on the training data.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11425\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1.png\" alt=\"\" width=\"586\" height=\"151\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1.png 586w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-300x77.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-110x28.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-200x52.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-380x98.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-255x66.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-550x142.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image1-150x39.png 150w\" sizes=\"(max-width: 586px) 100vw, 586px\" \/><\/p>\n<p><b>Step 5:<\/b><span style=\"font-weight: 400;\"> Anomaly Detection and Evaluation: Detect anomalies in the testing data and evaluate the model\u2019s performance.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11430\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1.png\" alt=\"\" width=\"574\" height=\"213\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1.png 574w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-300x111.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-110x41.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-200x74.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-380x141.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-255x95.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-550x204.png 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/09\/image4-1-150x56.png 150w\" sizes=\"(max-width: 574px) 100vw, 574px\" \/><\/p>\n<p><b>Step 6:<\/b><span style=\"font-weight: 400;\"> Interpretation and Tuning Interpret the results, analyse the classification report, and adjust the parameters if needed.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The contamination parameter in the Isolation Forest determines the expected proportion of anomalies in the data. You can adjust this based on your dataset\u2019s characteristics.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You can explore other algorithms like one-class SVM, autoencoders, or different ensemble methods for anomaly detection.<\/span><\/p>\n<p><b>Step 7:<\/b><span style=\"font-weight: 400;\"> Deployment Once you\u2019re satisfied with the model\u2019s performance, you can deploy it to detect anomalies in real-time data. This might involve setting up a pipeline to preprocess incoming data and use the trained model to predict anomalies.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Remember that anomaly detection is a continuous process. As new patterns of anomalies emerge, you\u2019ll need to update and retrain your model to ensure its effectiveness.<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In conclusion, we have provided you with an in-depth understanding of Anomaly Detection Machine Learning. Make sure that you learn the entire process thoroughly with much practice using Python.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In case you want to opt for a <\/span><a href=\"https:\/\/www.pickl.ai\/course\/free-machine-learning-certification-program\"><span style=\"font-weight: 400;\">free Machine Learning Certification course<\/span><\/a><span style=\"font-weight: 400;\"> to learn anomaly detection, you can apply for the same through Pickl.AI. With recorded sessions and lifetime access to the learning material, you will gain an expertise in anomaly detection.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"what-are-the-different-types-of-anomaly-detection-algorithms\"><span class=\"ez-toc-section\" id=\"What_are_the_Different_Types_of_Anomaly_Detection_Algorithms\"><\/span><b>What are the Different Types of Anomaly Detection Algorithms?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">There are two main categories: statistical methods and machine learning techniques. Statistical methods analyse historical data to identify deviations from the expected patterns. Machine learning algorithms, like k-Nearest Neighbors or Isolation Forests, learn from data to identify anomalies without relying on pre-defined patterns.<\/span><\/p>\n<h3 id=\"how-do-i-choose-the-right-anomaly-detection-algorithm\"><span class=\"ez-toc-section\" id=\"How_Do_I_Choose_the_Right_Anomaly_Detection_Algorithm\"><\/span><b>How Do I Choose the Right Anomaly Detection Algorithm?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The best algorithm depends on your data and specific needs. Consider factors like the type of data (numerical vs. categorical), the expected distribution of anomalies (rare vs. frequent), and the desired level of interpretability.<\/span><\/p>\n<h3 id=\"is-anomaly-detection-foolproof\"><span class=\"ez-toc-section\" id=\"Is_Anomaly_Detection_Foolproof\"><\/span><b>Is Anomaly Detection Foolproof?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">No algorithm is perfect. Anomalies can missed (false negatives) or normal data can be flagged incorrectly (false positives). Careful evaluation and tuning of the chosen algorithm are crucial to optimize its effectiveness for your specific use case.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\">\n","protected":false},"excerpt":{"rendered":"Anomaly Detection: Spot Hidden Patterns with Machine Learning.\u00a0\n","protected":false},"author":8,"featured_media":11433,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[2],"tags":[1629,1632,1634,1633,1635,1630,1631],"ppma_author":[2176,2185],"class_list":{"0":"post-4738","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-anomaly-detection-machine-learning","9":"tag-anomaly-detection-machine-learning-algorithms","10":"tag-anomaly-detection-machine-learning-example","11":"tag-anomaly-detection-machine-learning-python","12":"tag-anomaly-detection-using-machine-learning-in-python","13":"tag-machine-learning-anomaly-detection-example","14":"tag-types-of-anomaly-detection-in-machine-learning"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.3) - 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