4. Clustering and Regression

Workflow: Clustering and Regression

This workflow reads the two datasets (training and test set) created in the workflow "1. Data Preparation" and shows a few more machine learning algorithms:

1. A linear regression to predict the number of hours/week given all other attribute values (Linear Regression Learner and Regression Predictor nodes).

2. A k-Means clustering to detect patterns in the dataset by grouping together the most similar data rows.

Remember, that k-Means, like Neural Networks, needs normalized data.

The Statistics node mainly produces general statistical measures about one data column, including a roughly drawn histogram.

Reading data

Training and test set created in workflow "1. Data Preparation".

Training machine learning models

Applying trained models

Preprocessing

The k-Means algorithm needs normalized data

Calculating statistics