Graph and Natural Language Processing Based Recommendation System for Choosing Machine Learning Algorithms
K. T. Y. Mahima, Tilak Ginige · 2020
Machine Learning is a subset of Artificial Intelligence(AI). It provides the systems with the ability to learn automatically and perform independently without being programmed. When concentrating on the machine learning project choosing an algorithm is one of the most important steps. Machine learning algorithms are the math and logic that adjust the training of the model and the performance. When it comes to a machine learning project the choosing a correct algorithm for implementing the model is a big task. If the developer doesn't choose the suitable and most efficient algorithm for the program the accuracy of the program can be decreased. When we look at these machine learning algorithms there is no solution or no approach that fits for all. Several factors affect when choosing a machine learning algorithm such as the number of data, type of the problem ..etc. Most of the time a developer chooses the machine learning algorithm using his prior experience or analyzing several past similar projects. However, when it comes to a beginner it can be a tough experience. Most of the time beginners try with several algorithmic approaches to implement their model without any understanding. However, to avoid this there is no proper solution. Therefore here in this research, proposed an NLP and graph analytics-based approach for recommending a machine learning algorithm for a project. The summary of the proposed solution is here it analyzes past algorithms used in machine learning projects which are stored in a graph using NLP based keyword analysis and recommend the most suitable algorithmic approach. When the user inputs his project idea by using the Natural Language processing it generates keywords for the project description. Thereafter the system analyzes the graph which stores past machine learning projects and used technologies to find the most suitable algorithmic approach to the users' project. Moreover, it shows how the proposed algorithms were used in past similar projects. Therefore by using this system the developers can get a clear idea of the algorithm approach that they need to choose.