KNN & Naive Bayes Complete Handwritten Notes | Scaling, Distance Metrics, Confusion Matrix, Examples, GridSearchCV, Pipeline

These are clean, easy-to-understand handwritten notes covering KNN (k-Nearest Neighbors) and Naive Bayes, perfect for ML beginners, exam prep, and quick revision. Content taken from the PDF includes: Data Transformation & Scaling (Min-Max, Z-Score) — Page 1 StandardScaler workflow (fit, transform, avoiding leakage) — Page 1 Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score — Page 2 Naive Bayes Theory: Bayes Theorem, Conditional Independence, P(y|x), feature-wise probability multiplication — Page 3 Types of Naive Bayes: Gaussian, Multinomial, Bernoulli — Page 3 KNN Concepts: Classification & Regression, scaling, selecting K, distance to neighbors — Page 4 Distance Metrics: Euclidean, Manhattan — Pages 4–5 Limitations of KNN (outliers, scaling sensitivity, curse of dimensionality) — Page 5 KNN Code Example using sklearn — Page 5 Cross Validation, GridSearchCV & Pipeline Examples — Page 6 These notes are concise, exam-oriented, visual, and beginner-friendly, making them ideal for students looking for quick understanding + interview-ready revision.