The workflow trains a Linear Regression model to predict the price of a Lego set based on it's various features. It includes the process of:
- Loading the Lego dataset and cleaning it to remove missing values and outliers
- Remove collinearity between independent variables
- Partitioning the dataset into train and test dataset
- Modelling a Linear Regressor and prediciting sales of lego sets in test data
- Calculate the accuracy metrics of the model
- Plot residual plot and histogram to visualize Linear Regression assumptions of homoscedasticity (constant variance) and normal distribution of error.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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