An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals and researchers to find relevant research articles.

A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

A fast and flexible framework for data reduction in R

A DataCamp course for the University of Helsinki

Analysis of Data Scientist Job Descriptions using Natural Language Processing

Visually explore a game of semantle using a UMAP representation of the underlying word2vec word embedding.

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

Here we present a way to cluster images using Keras (VGG16), UMAP & HDBSCAN

To use deep learning to identify patients whose tumor DNA mutations “look similar to” other tumors for which treatments are effective.

PCA and Gaussian Mixture Model Clustering to uncover hidden segments in customer purchase data.

Analysis on hospital readmission data for diabetic patients using R and Tableau

Classifying users on social media, using text embeddings from OpenAI and others

The Jōyō Kanji Map

Predicting Nobel Physics Prize winners. Final project for Harvard CS109a 2017 edition /~https://github.com/covuworie/a-2017.

Artist Similarity Mapping

Data Exploration, and Analysis of accidents in the UK using Unsupervised ML algorithms

Principal Component Analysis is One of the Most Popular Dimensionality Reduction Algorithms used in Machine Learning Which comes under Unsupervised Way of Learning. It is also Used as a way of Feature Extraction where, More Information is Extracted from all the Existing Attributes, in just some 3-4 Attributes using the Concepts of Eigen Values a…

Analyze customers using unsupervised learning, PCA and K-Mean Clustering of Arvato dataset

This repository contains some supplementary materials (code, data and plots) for the paper “Resilience Potential of the Russian Arctic Cities".