I Got 95% Accuracy… And It Was Completely Useless

Educational tutorial about machine learning evaluation metrics. Author describes training a model achieving 95% accuracy that failed in real-world deployment. Uses spam detection example: a model predicting everything as 'not spam' achieves 95% accuracy on imbalanced dataset (95% legitimate emails, 5% spam) despite being useless. Explains the problem of imbalanced datasets where accuracy becomes misleading. Recommends using precision (correct positive predictions), recall (actual positives caught), and F1 score (harmonic mean) instead. Includes Python code examples using sklearn library. Argues machine learning success requires solving actual problems correctly, not achieving high accuracy scores. Emphasizes need to ask 'Is my model useful?' rather than relying on single metrics. Discusses real-world applications: fraud detection, medical diagnosis, spam filtering. Series installment on common ML mistakes for beginners.