How would you approach building an unsupervised learning model to detect outliers in a dataset with 6 behavioral variables and 25 contextual attributes, which needs normalization? Additionally, what evaluation metrics and visualizations would you use to measure the performance of the model?

Very hard Difficulty

Very-hard questions are the ultimate test of your expertise and preparation. They demand not just knowledge, but creativity and strategic thinking, often addressing unique or highly technical aspects of your field.

ML Case question

ML Case questions present machine learning scenarios to assess your applied AI knowledge. Demonstrate your approach to framing problems, selecting appropriate models, evaluating performance, and deploying machine learning solutions effectively.

Here's a Hint

Structure your answer around preprocessing (normalization strategies), model selection (isolation forest, LOF, etc.), and evaluation techniques that address the challenge of not having ground truth labels.