📌 Anomaly Detection Summary
Anomaly detection is a technique used to identify data points or patterns that do not fit the expected behaviour within a dataset. It helps to spot unusual events or errors by comparing new information against what is considered normal. This process is important for finding mistakes, fraud, or changes that need attention in a range of systems and industries.
🙋🏻♂️ Explain Anomaly Detection Simply
Imagine you have a basket of apples and most are red, but suddenly you find a green one. Anomaly detection is like noticing that green apple in a sea of red. It helps you quickly spot things that do not belong, so you can check if something is wrong or just different.
📅 How Can it be used?
Anomaly detection can be used in a project to automatically flag unusual transactions in financial records for further review.
🗺️ Real World Examples
In banking, anomaly detection is used to monitor credit card transactions. If a sudden purchase appears in a foreign country or a large sum is spent outside a person’s normal spending habits, the system flags this activity for possible fraud, helping protect customers and banks from financial loss.
In manufacturing, sensors track the performance of machinery. Anomaly detection identifies irregular patterns such as unexpected spikes in temperature or vibration, allowing maintenance teams to fix problems before equipment fails and production is disrupted.
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