[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116935-en":3,"doc-seo-116935-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116935,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","Machine Learning and Artificial Intelligence in Data Visualization - Thesis Report","This thesis builds an add-on for the LightningChart JS library that enables working with machine learning models in an interactive data-visualization context. The work targets readers who are new to machine learning while remaining applicable to a broad set of real-world use cases. Implementation uses JavaScript and TensorFlow JS for model execution, while LightningChart JS is used to test and visualize outputs. The result is three JavaScript classes for supervised learning models supporting both regression and classification, designed to be highly customizable and extensible for future development.","THESIS REPORT – DEGREE PROGRAMME IN INTERNET OF THINGS  \nTECHNOLOGY, COMMUNICATION AND TRANSPORT  \nMachine Learning and Artificial Intelligence in Data Visualization  \nA U T H O R / S : Pavel Romanov  \nSAVONIA UNIVERSITY OF APPLIED SCIENCES THESIS  \nAbstract  \n\n| Field of Study\u003Cbr>Technology, communication and transport |  |\n| --- | --- |\n| Degree Programme\u003Cbr>Degree Programme in Information Technology, Internet of Things |  |\n| Author(s)\u003Cbr>Pavel Romanov |  |\n| Title of Thesis\u003Cbr>Machine Learning and Artificial Intelligence in Data Visualization |  |\n| Date 28 April 2023 | Pages/Appendices 32 |\n| Client Organisation /Partners\u003Cbr>LightningChart Oy |  |\n| Abstract\u003Cbr>The purpose of this thesis was to create an add-on for the LightningChart JS library to work with machine learning models. The thesis is aimed to be understandable for people unfamiliar with machine learning, while also being suitable for a wide range of applications.\u003Cbr>The project was implemented using JavaScript as a programming language and the TensorFlow JS library for machine learning implementation. The LightningChart JS charting library was used to test and visualize the results of the project.\u003Cbr>As a result of this thesis, three JavaScript classes representing various supervised machine learning models were created and tested on real use cases. They are suitable for both small and large projects and can solve both regression and classification problems. The results of this thesis are highly customizable and can serve as a base for future work. |  |\n| Keywords\u003Cbr>Machine learning, data visualization, linear regression, logistic Regression, K-Nearest Neighbors, lightningchart js |  |\n\nCONTENTS  \n1 INTRODUCTION ................................................................................................................................. 5  \n1.1 Structure ..................................................................................................................................... 5  \n1.2 Project relevance .......................................................................................................................... 6  \n2 BACKGROUND INFORMATION...............................................................................................................7  \n2.1 Machine learning ........................................................................................................................... 7  \n2.1.1 Types of machine learning...............................................................................................7  \n2.1.1.1 Supervised learning................................................................................................ 7  \n2.1.1.2 Unsupervised learning ............................................................................................ 7  \n2.1.1.3 Semi-supervised learning........................................................................................ 8  \n2.1.1.4 Reinforcement learning .......................................................................................... 8  \n2.1.2 Choosing machine learning models ..................................................................................8  \n2.1.3 Final model selection ....................................................................................................... 9  \n2.2 Algorithms ................................................................................................................................... 9  \n2.2.1 K-Nearest Neighbors algorithm .......................................................................................10  \n2.2.2 Solving linear regression with gradient descent algorithm .................................................10  \n2.2.3 Solving logistic regression with gradient descent algorithm...............................................12  \n3 IMPLEMENTATION ............................................................................................................................ 14  \n3.1 Tensor","cbCaiaMX2wjRVn9u","https://ap.wps.com/l/cbCaiaMX2wjRVn9u","pdf",1026149,1,32,"English","en",105,"# Introduction\n## Structure\n## Project relevance\n# Background information\n## Machine learning\n## Algorithms\n# Implementation\n## TensorFlow JS\n## KNN implementation\n## Linear regression implementation\n## Logistic regression implementation\n# Testing\n## LightningChart JS\n## KNN classification app example\n## KNN regression app example\n## Linear regression app example","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"The thesis aims to create an add-on for the LightningChart JS library that can work with machine learning models and help visualize their results.\"},{\"question\":\"Which tools and technologies were used to implement the project?\",\"answer\":\"The implementation uses JavaScript together with TensorFlow JS for machine learning, and LightningChart JS to test and visualize the results.\"},{\"question\":\"What types of machine learning problems does the delivered solution support?\",\"answer\":\"The project provides three JavaScript classes for supervised learning models that can solve both regression and classification tasks.\"}]","Machine Learning and Artificial Intelligence in Data Visualization - 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