KNIME Data Science Learnathon - Group 1 - Data Access and Data Manipulation (Solution)

0 ×

Solution to the tasks for Group 1 in KNIME Data Science Learnathon

- Access data
- Preprocess data by filtering rows, filtering columns, converting column types, and handling missing values
- Join data from two different sources
- Generate new features by binning and by a rule
- Remove outliers
- Normalize data
- Partition data into a training and a test set
- Write data into a file

URL: Will They Blend? The Blog Post Collection - KNIME Press https://www.knime.com/knimepress/will-they-blend
URL: Dimensionality Reduction and Feature Selection - KNIME Blog https://www.knime.com/blog/seven-techniques-for-data-dimensionality-reduction
URL: Four Techniques for Outlier Detection - KNIME Blog https://www.knime.com/blog/four-techniques-for-outlier-detection
URL: KNIME Learning Center - KNIME.com https://www.knime.com/knime-introductory-course/chapter3
URL: What is KNIME Analytics Platform - KNIME TV (YouTube) https://youtu.be/5LWMc8WZmdw
URL: KNIME Cheat Sheets - KNIME.com https://www.knime.com/cheat-sheets

Nodes

• Rule-based Row Filter4 ×
• Missing Value3 ×
• Row Filter3 ×
• Table Row to Variable3 ×
• Date&Time-based Row Filter2 ×
• Show all 24 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Excel Support
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Statistics Nodes

Links

• No links available