Icon

04_​Data_​Preprocessing_​for_​ML_​Models

Data Preprocessing for ML Models

This workflow demonstrates the following standard preprocessing steps before training a machine learning model:
- Partitioning
- Outlier detection
- Missing value handling
- Dimensionality reduction
- Conversion
- Feature selection

URL: House Prices Dataset https://www.kaggle.com/marcopale/housing

This workflow demonstrates the standard preprocessing steps before training a machine learning model.
Normalization
Conversion
Partition randomly 80% for training 20% for test
Table Partitioner
Feature Selection
Missing Values Handling
Dimensionality Reduction
asdf
CSV Reader
Outlier Detection

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: Lada.Rudnitckaia
Created at: 2021-08-18
On NodePit since: 2025-10-08
Last update: 2025-10-08
Created with KNIME version: v5.7.0
Tags: preprocessingpartitioningoutlier detectionmissing valuePCAencodingconversionforward feature selectionmachine learningML

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

Try NodePit Runner!