Build a model to predict wine quality
Overview
This question evaluates predictive modeling and data-science competencies—exploratory data analysis, feature assessment, problem framing (regression vs classification vs ordinal), model selection and validation, evaluation metric choice, and post-model feature-importance interpretation—in the Machine Learning domain, combining conceptual understanding with practical application. It is commonly asked in technical interviews for Data Scientist roles because it tests end-to-end modeling judgment, reasoning about data distributions and variable usefulness, selection of validation and evaluation strategies, and awareness of pitfalls such as collinearity and data leakage.
Modeling task: Predict wine quality from a CSV
You are given a clean CSV dataset about red wine. The target (dependent) variable is:
-
quality(integer): wine quality score on a 1–7 scale.
There are ~10 input (independent) variables describing the wine’s chemical properties (all numeric), e.g.:
-
fixed_acidity(float) -
volatile_acidity(float) -
citric_acid(float) -
residual_sugar(float) -
chlorides(float) -
free_sulfur_dioxide(float) -
total_sulfur_dioxide(float) -
density(float) -
pH(float) -
sulphates(float) -
alcohol(float)
Assume:
- There are no missing values .
- Each row is one wine sample; samples are i.i.d. (unless you discover evidence otherwise).
Questions
- EDA: What do you learn from exploring the dataset (distributions, outliers, correlations, target imbalance, non-linearities)? List at least 3 concrete findings and how they affect modeling choices.
-
Feature usefulness (pre-model):
Which variables appear likely to be useful for predicting
quality, and why? Mention at least two different ways to assess this (e.g., correlation, mutual information, monotonic trends, domain reasoning). -
Modeling:
Build a model to predict
quality. You may choose any approach. Clearly specify:- whether you treat the task as regression , classification , or ordinal classification , and why
- train/validation strategy (e.g., split or cross-validation)
- evaluation metric(s)
- Feature importance (post-model): How would you determine which variables are actually useful in your final model? Provide a method appropriate to your model choice and explain pitfalls (e.g., collinearity, leakage, bias in impurity-based importances).
Deliverable: a brief write-up of your approach and results; optionally include pseudocode / a code outline in Python (pandas + scikit-learn).
Solution
Login required