Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages

The article compares machine learning tools using the example of several popular programming languages. Existing tools in the following programming languages were tested and compared with each other: Python, Java, R, Julia, C#. For the needs of article, algorithms were created in each studied language, operating on the same test set and using algorithms from the same group. The collected results included the program's running time, number of lines of code and accuracy of trained model. Based on the obtained data, conclusions were drawn that interpreted language libraries in terms of creating machine learning solutions are more effective than compiled language libraries.