Tutorial 02: Wine Quality EDA

Level: Beginner · Part of: ML Learning Roadmap

← Back to roadmap

Case Study: Winery Quality Assessment

Scenario

A winery wants to understand which chemical properties (alcohol, acidity, sulphates) correlate with wine quality before building a predictive model.

Learning Objectives

• Load and inspect the Wine dataset
• Compute summary statistics with NumPy/pandas
• Visualize feature distributions
• Identify class imbalance across cultivars

Prerequisites

• Tutorial 01
• Python 3.9+, scikit-learn, NumPy, pandas, matplotlib

Dataset

load_wine() — 178 samples, 13 chemical features, 3 cultivar classes

Hands-On Solution

Copy and run the complete script below:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_wine

wine = load_wine()
X, y = wine.data, wine.target

print("Feature names:", wine.feature_names)
print("Target names:", wine.target_names)
print(f"Samples per class: {np.bincount(y)}")

# Summary statistics
for i, name in enumerate(wine.feature_names):
    col = X[:, i]
    print(f"{name:20s} mean={col.mean():.2f}  std={col.std():.2f}")

# Visualize alcohol vs flavanoids colored by cultivar
plt.figure(figsize=(8, 5))
for cls in range(3):
  mask = y == cls
  plt.scatter(X[mask, 0], X[mask, 6], label=wine.target_names[cls], alpha=0.7)
plt.xlabel("Alcohol")
plt.ylabel("Flavanoids")
plt.legend()
plt.title("Wine Cultivars: Alcohol vs Flavanoids")
plt.tight_layout()
plt.savefig("wine_eda.png", dpi=120)
print("Saved wine_eda.png")

Expected Output

When you run the script, you should see evaluation metrics printed to the console. Some tutorials also save .png plot files in the current directory.

Exercises

1. Create a histogram of the proline feature for each cultivar.
2. Which two features have the highest correlation? Use np.corrcoef.
3. Count how many samples belong to each class and discuss imbalance.

Key Takeaways

• EDA before modeling prevents surprises later
• Feature scales differ widely — scaling will matter for distance-based models
• Visualization reveals separability between classes

Navigation

Part of the ML Learning Roadmap — Hands-On with scikit-learn