"## ======= Question 3 (3 Point) ======= ", "## Q3-1. Split data into 70% train and 30% test data. ", "## Q3-2. Based on train data, build a linear regression model to predict wine quality (numeric) using all available predictor variables. ", "## Note that the target variables (quality and quality_binary) should not be included as predictors. ", "## Q3-3. Based on test data, evaluate the predictive model based on RMSE (Root Mean Squared Error). " "## ======= Question 4 (3 Point) ======= ", "## Q4-1. Based on train data, build a logistic regression model to classify the binary wine quality (\"quality_binary\") using all available predictor variables. ", "## Note that the target variables (quality and quality_binary) should not be included as predictors. ", "## Q4-2. Show the accuracy and confusion matrix of the model" "## ======= Question 5 (2 Point) ======= ", "## Many wine experts argue that acid is a vital component of wine. ", "## Q5-1. Build an another logistic regression model to predict wine quality using all predictors except acidity measures ( fixed.acidity, volatile.acidity, citric.acid, and pH). ", "## Q5-2. By comparing the model performance with the full model above (Question 4), do you agree that acid is a significant predictor for wine quality (sensory preference)? "