KNN Algorithm from Scratch -Cat vs Dog Image Classification in Python (Complete Experiment)
🧠 KNN Algorithm from Scratch — Real Image Classification Experiment
I recently built a K-Nearest Neighbors (KNN) algorithm from scratch in Python to perform cat vs dog image classification.
This project focuses on understanding how distance-based machine learning really works, rather than simply chasing accuracy.
🎯 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.
This happens because:
- 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 this makes you a much stronger machine learning engineer.
📦 Complete Source Code
You can explore the full project here:
🔗 https://github.com/yogender-ai/knn-cat-dog-demo
🏁 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.
