Question

give specific scenarios (real life, if possible) where you can apply knn to detect anomalies/outliers and do face recognition.

Answer 1

KNN can be used to detect anomalies/outliers in credit card fraud detection, network intrusion detection, and manufacturing. It can also be applied in face recognition systems for classifying and identifying faces.

Step-by-step explanation:

Detecting Anomalies/Outliers:

K-nearest neighbors (KNN) algorithm can be used to detect anomalies or outliers in various real-life scenarios. For example:

In credit card fraud detection, KNN can be used to identify abnormal patterns in transactions by comparing them to normal patterns.

In network intrusion detection, KNN can be used to detect abnormal network behavior by comparing it to normal network traffic.

In manufacturing, KNN can be used to identify defective products by comparing their features to the features of normal products.

Face Recognition:

KNN can also be utilized in face recognition systems. For instance:

1. Given a database of labeled face images, KNN can be used to classify a new face image by comparing its features to the features of known faces.
2. KNN can be employed in surveillance systems to match detected faces against a database of known individuals for identification purposes.