Completed Assignment 1

[iverbova]

What changes are you trying to make?

Added completed code to assignment_1.ipynb, including:

• Reading and inspecting the Wine dataset (rows, columns, data types)
• Standardizing predictor variables
• Splitting data into training and test sets using train_test_split
• Performing hyperparameter tuning with GridSearchCV (1–50 neighbors, 10-fold CV)
• Evaluating the model's accuracy on the test set

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What did you learn from the changes you have made?

I learned how to prepare data for machine learning using KNN, including:

• The importance of standardization for distance-based models
• How to apply reproducible data splits using a random seed
• How to use grid search and cross-validation to tune hyperparameters
• How to evaluate classifier performance using accuracy

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

N/A

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

Yes:

• I initially got no output from the train/test split. I resolved this by using .describe() and .shape to confirm the data was correctly split.
• Understanding why standardization and seed-setting were necessary took some effort, but was clarified by reviewing scikit-learn documentation and working through examples.

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How were these changes tested?

All code cells were run in a local Jupyter Notebook environment and confirmed to:

• Execute from top to bottom without errors
• Correctly split and standardize data
• Return the best n_neighbors from cross-validation
• Evaluate accuracy on the test data

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

N/A

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Checklist

• I can confirm that my changes are working as intended