Build a model to predict wine quality

Quick 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.

Company: EvenUp

Role: Data Scientist

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

## 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 1. **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. 2. **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). 3. **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) 4. **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).

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).

Build a model to predict wine quality

Quick Overview