Linear_​Regression_​Solution

Exercise: Linear Regression

In this exercise we will predict the price of an house in Ames (Iowa, USA) given a number of features (size, neighborhood, heating...) using Linear Regression.

1. Read dataset AmesHousing_simple.csv. It contains information about houses sold in Ames (only numerical values) as well as the SalePrice.

2. Add Partitioning node to File Reader output.

   • Top port should have 70 % of the rows

   • Draw randomly such rows

3. Add Linear Regression Learner to top output port of Partitioning node

   • Select price column to be learned.

   • Execute the node and open its scatter plot view. Which column is most correlated to the price (column selection tab)?

4. Add Regression Predictor

   • Predict test set (remaining 30% rows) by simply connecting the remaining unconnected output ports

5. Remove rows with missing prediction

6. Add Numeric Scorer to Regression Predictor Output

   • Reference Column: the column you learned.

   • Predicted Column: the new column created by the predictor node