Journal 44
June 3 - June 9 This week, I learned more about linear regression and the complete machine learning workflow involved in building and evaluating a predictive model. We went over how to prepare data for machine learning by handling missing values, creating new variables, encoding categorical variables using dummy variables, and splitting the data into training and testing sets. I also learned how to train a linear regression model, interpret the model coefficients, generate predictions, and evaluate the model using metrics such as mean squared error (MSE) and mean absolute error MAE). I really enjoyed learning about the importance of comparing a model's performance to a baseline prediction. Before this week, I understood that lower error values were ideal, but I had not thought about how to determine whether a model was actually providing useful predictions. Comparing our linear regression model to a simple baseline that predicted the average value helped demonstrate that the mode...