An implementation of a parsimonious K-NN model that yeilds a sizeable performance boost over standard K-NN algorithms from Scikit-Learn.
I placed all the notebooks I used to create this model into the "Model Development" folder for my reference.
The final model, along with its analysis and comparison to the K-NN model offered by Scikit-Learn is in the
"Cosine-Similarity Model Analysis" notebook.
The Randomized K-NN and Superior K-NN notebooks seek to implement a K-NN style algorithms that leverage methods analogous to iDistance for image classification. These two notebooks show that these new classification algorithms classify datapoints far faster than standard "brute force" and tree based methods (like KD-Tree or Ball Tree), however they unfortunately have relatively low classification accuracy.