Nearest Neighbor Search with Neighborhood Graph and Tree for High-dimensional Data

Interactive K-Nearest Neighbors machine learning algorithm in JavaScript.

Java library for approximate nearest neighbors search using Hierarchical Navigable Small World graphs

Learning to create Machine Learning Algorithms

GloVe word vector embedding experiments (similar to Word2Vec)

fair comparison for recommender system benchmarks

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

Rcpp bindings for the approximate nearest neighbors library hnswlib

Today, using machine learning algorithms is as easy as "import knn from ..." but it doesn't really help if you want to learn how the algorithms work

Machine Learning Algorithms on NSL-KDD dataset

The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.

Model-Free-Episodic-Control implementation.

OpenCV-Python Tutorials

Python machine learning algorithm implementation including Gradient Descent, Newton Method, Gaussian Discriminant Analysis, Naive Bayes, SVM, Decision Tree, Random Forest, K Means, KNN

This project consists of implementations of several kNN algorithms for road networks (aka finding nearest points of interest) and the experimental framework to compare them from a research paper published in PVLDB 2016. You can use it to add new methods and/or queries or reproduce our experimental results.

Being able to perform gameplay analysis of NBA players, NBA Predictive Analytics is a basketball coach's new best friend.

knn with cosine similarity (distance)

A C implementation of "Efficient K-Nearest Neighbor Graph Construction for Generic Similarity Measures"

An incomplete library for extracting 3D data from a 2D image. It is no longer being developed or maintained. See "Constellation-2.0" for an improved and complete version

An implementation of a density based outlier detection method - the Local Outlier Factor Technique, to find frauds in credit card transactions. For detecting both local and global outliers.

Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.

this repository is created to learn how implement KNN on specific purpose

Breast Cancer Wisconsin (Diagnostic) Prediction Using Various Architecture, though XgBoost Classifier out performed all

An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.

K-NN is a basic classification algorithm that can classify a data using its distance to other data points. I wrote a KNN algorithm without using any Machine Learning libraries such as scikit-learn etc.

Build my own K-nearest-neighbors algorithm and compare the accuracy with KNeighborsClassifier