Icon

07.02 Outlier Detection solution

<p>07.02 Outlier Detection - solution<br><br>[L4-ML] Machine Learning Algorithms - Specialization</p><p>07 Data Cleaning Preparation<br>Detect and remove outliers in the data using the following techniques:<br>- Numeric outliers outside the upper/lower whiskers of a box plot<br>- Outliers in the distribution tails (z-score)</p>

URL: Ames Housing Dataset on kaggle https://www.kaggle.com/prevek18/ames-housing-dataset
URL: Description of the Ames Iowa Housing Data https://rdrr.io/cran/AmesHousing/man/ames_raw.html

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2025-03-14
Last update: 2025-03-31
Created with KNIME version: v5.4.0
Tags: data manipulationpreprocessingoutlier detectionz-scoreeducation

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!