Build a model to predict wine quality

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).