# Introduction In this lab, we will perform outlier detection on a real dataset using scikit-learn. Outlier detection is the process of identifying data points that significantly differ from the majority of the data. Outliers can be caused by measurement errors, data corruption, or simply represent a rare event. Outlier detection is important in many applications, such as fraud detection, network intrusion detection, and medical diagnosis. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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