I gained an understanding of the assumptions (linearity, homoscedasticity, multivariate normality, independence of observations, lack of multicollinearity)  underlying linear regression and situations where it may not be applicable. I also explored the importance of avoiding the dummy variable trap. Additionally, I conducted multiple linear regression analysis on sample data using the scikit-learn library in Python within Jupyter Lab. Furthermore, I familiarized myself with five methods of model building (All-in where you throw in all the predictors, Backward Elimination, Forward Selection, Bidirectional Elimination, All-possible-models).