Lab 04: Wine Quality

Scenario: You work for a startup that wants to create an AI Sommelier. Rather than using a highly trained human, instead, you will purchase chemistry equipment to generate physicochemical data for wine, and train models based on previous wine quality reviews by human sommeliers.


The goal of this lab is to create a model that predicts the quality of a wine given its physicochemical characteristics.

  • Note: We do not state if this is a classification or regression task. In practice, either strategy could be used.


This lab will use data from the UC Irvine Machine Learning Repository.


  • quality


  • fixed acidity
  • volatile acidity
  • citric acid
  • residual sugar
  • chlorides
  • free sulfur dioxide
  • total sulfur dioxide
  • density
  • pH
  • sulphates
  • alcohol
  • color

Data in Python

To load the data in Python, use:

import pandas as pd
wine_train = pd.read_csv("")

To create the X and y variants of the training data, use:

# create X and y for train
X_train = wine_train.drop("quality", axis=1)
y_train = wine_train["quality"]

Sample Statistics

Before modeling, be sure to look at the data. Calculate the summary statistics requested on PrairieLearn and create a visualization for your report.


For this lab you will select one model to submit to the autograder. You may use any modeling techniques you’d like. The only rules are:

  • Models must start from the given training data, unmodified.
    • Importantly, the types and shapes of X_train and y_train should not be changed.
    • In the autograder, we will call mod.predict(X_test) on your model, where your model is loaded as mod and X_test has a compatible shape with and the same variable names and types as X_train.
    • We assume that you will use a Pipeline and GridSearchCV from sklearn as you will need to deal with heterogeneous data, and you should be using cross-validation.
      • So more specifically, you should create a Pipeline that is fit with GridSearchCV. Done correctly, this will store a “model” that you can submit to the autograder.
  • Your model must have a fit method.
  • Your model must have a predict method that returns numbers.
  • Your model should be created with scikit-learn version 1.4.0 or newer.
  • Your model should be serialized with joblib version 1.3.2 or newer.
    • Your serialized model must be less than 5MB.

While you can use any modeling technique, each lab is designed such that a model using only techniques seen so far in the course can pass the checks in the autograder.

To obtain the maximum points via the autograder, your model performance must meet or exceed:

Test MAE: 0.47

Model Persistence

To save your model for submission to the autograder, use the dump function from the joblib library. Check PrairieLearn for the filename that the autograder expects.


As always, be sure to state a conclusion, that is, whether or not you would use the model you trained and selected for the real world scenario described at the start of the lab! If you are asked to train multiple models, first make clear which model you selected and are considering for use in practice. Discuss any limitations or potential improvements.

Additional discussion topics:

  • Generally comment on the real-world applicability of this model. Is the cost of the chemistry equipment and processes worth removing humans from this process? Can this “AI” replace all aspects of a sommelier?
  • The quality data given are integers. Are you predictions integers? If not, is that a problem?

When answering discussion prompts: Do not simply answer the prompt! Answer the prompt, but write as if the prompt did not exist. Write your report as if the person reading it did not have access to this document!

Template Notebook


Before submission, especially of your report, you should be sure to review the Lab Policy page!

On Canvas, be sure to submit both your source .ipynb file and a rendered .html version of the report.