Implement and Optimize a K-Nearest Neighbors Classifier

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- **Metric choice – “Which similarity metric will you use?”** - Chose cosine for text‑like vectors; mentioned Euclidean as alternative. - **Tie handling – “Why does the KNN return ‘Sports’ instead of ‘Technology’?”** - Explained Counter.most_common() tie‑break order; proposed weighted voting or explicit tie‑break by avg distance. - **Data ambiguity – “What if identical feature vectors have different labels?”** - Classified as data‑quality issue; suggested validation to deduplicate or keep most frequent label. - **Production robustness – “How would you handle noisy or overlapping data?”** - Outlined schema‑level checks, semantic stats, confidence thresholding, fallback heuristics. - **Feature scaling – “What about vastly different feature ranges?”** - Recommended Min‑Max or z‑score normalization before distance calculation. - **Aggregation – “Two identical‑category points – keep or aggregate?”** - Keep both for redundancy or replace with mean centroid for efficiency; justified mean choice. - **Mean vs. median – “When is mean not appropriate?”** - Outliers skew mean; would switch to median or trimmed mean in such cases.