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In this liveProject, you’ll use scikit-learn and the PyOD library to build an unsupervised machine learning model for detecting hyperthyroidism. PyOD is a Python toolkit for detecting outlying objects in multivariate data. You’ll compare performance between four different anomaly detection methods on a specialized thyroid dataset: PCA, Clustering-Based Local Outlier Factor (CBLOF), Histogram-Based Outlier Score (HBOS), and KNN algorithms.
This liveProject is for Python programmers who are interested in exploring machine learning. To begin this liveProject, you will need to be familiar with the following:
In this liveProject, you’ll master the domain of anomaly detection through exploring various methods.
geekle is based on a wordle clone.