Outlier Detection and Handling — Free Data Mining Tutorial
Learn Outlier Detection and Handling in Data Mining with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Outlier Detection and Handling in Data Mining with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Outlier Detection and Handling in Data Mining
Outliers are data points that deviate significantly from the pattern of the data. They can be caused by measurement errors, data entry mistakes, or genuine anomalies.
Detection methods include statistical approaches (Z-score, IQR), distance-based (Isolation Forest), and domain knowledge-based rules.
Handling outliers involves deciding whether to remove them, transform them, or keep them depending on whether they represent errors or valid extreme values.
Learn Outlier Detection and Handling step by step with Syllab's free interactive Data Mining tutorial — runnable code examples, practice exercises and instant AI feedback, all free with no signup. Explore the full Data Mining course →