Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)

Learning to create Machine Learning Algorithms

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

Essential NLP & ML, short & fast pure Python code

GloVe word vector embedding experiments (similar to Word2Vec)

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

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

Model-Free-Episodic-Control implementation.

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

This is the official code for our paper "Simple and Scalable Nearest Neighbor Machine Translation" (ICLR 2023).

knn with cosine similarity (distance)

Recommendation System sever for Projectube developed by insource team of VietCode company leveraging User-Based Collaborative Filtering

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

The FaceAB software is a graphical interface program that uses advanced machine learning algorithms to perform real-time face recognition, age prediction, and gender prediction. Developed by Mohamad Aboud

Car Accident Severity Analysis - Seattle Washington (Machine Learning Application)

"Instagram Hashtag Generator" analyzes an image and gives back some hashtags based on a prediction of binary classifiers. It is possible to use two different classifiers, a classifier based on Logistic Regression and the other one on k-nearest neighbors.

Spherical k-nearest neighbors interpolation (geospatial interpolator)

Introducing "spotify-recommender-api", a pip package that uses an adaptation of the KNN algorithm to make personalized song recommendations for users. It analyzes your Spotify listening stats and suggests new tracks you may enjoy, and also clusters similar songs on a big playlist in order to help one find songs that play well in a smaller playlist

This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.