1. Load the relevant libraries for machine learning.

2. Load wine data from scikit-learn.

3. Print the target names.

4. Print the names of the features.

5. Print the number of instances for each class.

6. Split the wine data into training and test sets.

7. Print the shapes of the training and test data.

8. Train a KNN model with k=1.

9. Test the accuracy of the model against the test set.

10. Print the minimum, maximum, and average accuracies.