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.
This is a companion discussion topic for the original entry at https://kni.me/w/W4r-5ayucobkHYVi