LCR - Assignment 1

[francesbruno]

My apologies for the late submission -- we had a death in the family last week so I had to take some time off to tend to personal affairs. Thanks in advance for your consideration.

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

Loading and inspecting the Wine dataset, identifying the response and predictor variables, standardizing the predictors, splitting the data into training and testing sets, tuning a KNN classifier using cross-validation, fitting the final model, and evaluating its performance using test accuracy.

What did you learn from the changes you have made?

How to apply K-nearest neighbors classification in Python using scikit-learn. I also practiced to separate predictors from the response variable, and how to use cross-validation to choose the best value of n_neighbors.

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

No

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

Initially understanding why the predictor variables needed to be standardized but the class variable should not be standardized...but then realizing that KNN uses distances between predictor values, while the response variable represents category labels.

How were these changes tested?

running the code line by line then run all once completed

A reference to a related issue in your repository (if applicable)

Checklist

• [ FB] I can confirm that my changes are working as intended

[juliagallucci]

accidently sent PR to main