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What is Anomaly Detection?

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Let's delve into the concept of data anomalies, their classification, and their impact on decision-making. Throughout this exploration, we will gain a comprehensive understanding of these topics, accompanied by real-life examples to illustrate their significance.

General Information

Types of Anomalies

How Outliers Influence On Prediction Results

What Should We Do With Detected Outliers

Statistical Methods in Anomaly Detection

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Statistical methods play a crucial role in various domains, including finance, cybersecurity, manufacturing, and healthcare, where detecting anomalies can prevent fraud, ensure data quality, and improve overall system reliability. These methods are particularly valuable when dealing with large datasets and complex systems, as they provide a systematic and quantitative approach to anomaly detection.

Rule-based Approach

Challenge: Rule-based Approach

1.5 IQR Rule

3-Sigma Rule

Median Absolute Deviation

Challenge: Outlier Detection Using MAD Rule

Machine Learning Techniques

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Now, let's explore how machine learning methods can effectively address anomalies. We will examine various techniques, including clustering, regularization, dropout, and more, that are employed for this purpose.

Clustering

Challenge: Using DBSCAN Clustering to Detect Outliers

Regularisation

Challenge: Solving Task Using Regularisation

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