This exercise focuses on learning about the relations among the data of prices of the houses in Boston.
Steps:
Read excel file with housing prices, available in folder data/
prices in the last column called "MEDV„ (median value of the occupied housed in 1.000 USD)
Check the relationships:
Check the relationship between the price "MEDV" and independent feature "LSTAT" which represents percentage of poor people
Check the relationship between the price "MEDV" and independent features "RM" representing the number of rooms, and "DIS" representing Distance of the employee centers
Depict an average MEDV price for different ages.
Before you do so, you should round the ages on whole numbers, group the MEDV based on the age and create a heat map
Workflow
Relations in data with scatter and bubble plots
External resources
Used extensions & nodes
All required extensions are part of the default installation of KNIME Analytics Platform version 4.2.1
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