How do I preprocess data for anomaly detection?

How do I preprocess data for anomaly detection?

Preprocessing data for anomaly detection involves several steps, including data cleaning, normalization, and feature selection.

What specific techniques should I use for data cleaning?

You can use techniques like handling missing values, removing duplicates, and filtering out outliers.

How do I normalize my data?

Normalization can be done using methods like Min-Max scaling or Z-score standardization to ensure uniformity.

What features are most important for detecting anomalies?

Important features often depend on the context, but look for those that show significant variance or patterns.