Apply K-means clustering to customer segmentation data and analyze results.
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Evaluation of code structure, comments, and documentation
Evaluate the code quality, including proper variable naming, code structure, comments, and documentation. Provide specific feedback on areas for improvement.
Assessment of correct algorithm implementation
Check if the algorithm is implemented correctly according to the theoretical foundations. Identify any logical errors or deviations from standard implementations.
Evaluation of mathematical concepts and formulations
Assess the student's understanding of the mathematical concepts behind the implementation. Check for correct use of formulas and mathematical reasoning.
Assessment of model performance evaluation
Evaluate how well the student analyzed the performance of their model, including appropriate metrics, validation techniques, and interpretation of results.
Evaluation of experimental methodology
Assess the experimental design, including data preprocessing, parameter tuning, and comparison methodology. Provide feedback on scientific rigor.