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-# OSML [![Build Status](https://travis-ci.org/ViktorC/OSML.svg?branch=master)](https://travis-ci.org/ViktorC/OSML) [![Quality Gate](https://sonarqube.com/api/project_badges/measure?project=net.viktorc:osml&metric=coverage)](https://sonarcloud.io/dashboard?id=net.viktorc:osml) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+# OSML [![Build Status](https://travis-ci.org/ViktorC/OSML.svg?branch=master)](https://travis-ci.org/ViktorC/OSML) [![Quality Gate](https://sonarcloud.io/api/project_badges/measure?project=net.viktorc:osml&metric=coverage)](https://sonarcloud.io/dashboard?id=net.viktorc:osml) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 A Python library of 'old school' machine learning algorithms such as linear regression, logistic regression, naive Bayes, k-nearest neighbors, decision trees, support vector machines, and meta models like bootstrap aggregating and gradient boosting. OSML uses the [pandas](https://pandas.pydata.org/) and [NumPy](http://www.numpy.org/) libraries for data manipulation and basic linear algebra operations.
 
 ## Models