KNN Algorithm from Scratch -Cat vs Dog Image Classification in Python (Complete Experiment)

Source: Dev.to

🎯 What this project demonstrates

• Machine learning from scratch
• KNN algorithm implementation
• Image classification using raw pixel vectors
• Supervised learning fundamentals
• How noise, distance, and neighbors affect predictions
• The curse of dimensionality in real experiments

🧪 The core experiment

After training the model, I discovered that removing only 5 noisy images from each class drastically improved prediction accuracy.

Why this happens:

• KNN has no abstraction or learning of features.
• All intelligence comes from the geometry of the data.
• A few bad samples can distort the entire decision boundary.

🧬 Why this matters

Most tutorials skip the uncomfortable truth about KNN:

• More data does not always mean better results.
• Distance metrics define the model’s intelligence.
• High‑dimensional data behaves very unintuitively.

Understanding these points makes you a much stronger machine learning engineer.

📦 Complete Source Code

(Insert source code or link to repository here)

🏁 Final thought

Before neural networks learn to see, we must understand how distance learns to lie.

If you’re learning machine learning, this experiment is worth exploring.