Challenge 13 - Understanding Crime and Real Estate Connections

0 ×

Level: Medium

Description: You ara a data scientist working for a real estate company, and heard a rumour that the "average number of rooms per dwelling" (RM) may be connected to the "per capita crime rate" (CRIM) for different towns. You then decide to investigate if this is the case for Boston, the city where you live and work from. To this end, you decide to experiment with a machine learning regression model and with a topic that you have recently been studying: XAI. How are RM and CRIM connected in Boston? Hint: Consider calculating the SHAP values of each independent feature using a SHAP loop. Hint 2: Consider using a dependence plot (https://hub.knime.com/-/spaces/-/latest/~mMx1llj39or_a-4G/) to verify how RM and CRIM are connected visually.

Dataset Link: https://www.kaggle.com/datasets/funxexcel/boston-housing-dataset-with-column-names

Nodes

• Component Input61 ×
• Component Output61 ×
• Column Filter56 ×
• Column Rename54 ×
• Rule Engine Variable45 ×
• Show all 137 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Deep Learning - Keras Integration
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME H2O Machine Learning Integration
• FeatureKNIME Integrated Deployment
• Show all 13 modules

Links

• No links available