Open Access
Forecasting Models of Natural Gas
Author(s) -
Meenakshi Thalor,
Ritesh Choudhary,
Ajay Jangid,
Deep Gandhecha,
Rishab Bhat
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst2182121
Subject(s) - computer science , debugging , field (mathematics) , upgrade , software , artificial intelligence , data science , machine learning , human–computer interaction , software engineering , programming language , mathematics , pure mathematics , operating system
In recent times, there has been immense research in the machine learning and artificial intelligence field. Resulting into a gigantic collection of research papers, well formatted textbooks and countless frameworks that have been developed. Even though individuals are open to such enormous resources, the best way to learn ML algorithms is to implement them. Individuals often find this difficult not only because of math, but also due to the exponentially difficult debugging, software upgrade patch or fix, and fear of programming for individual enthusiasts from other fields. Some of these difficulties can be eliminated by creating an online collaborative environment, which is setup free, provides a visual framework, and helps in understanding and implementing the basic and research algorithms. In this project, we are trying to create an online collaborative environment named “Visual Prediction”, which is an online application that promotes visual based learning and provides a GUI based ML framework. The platform will support collaborative learning for users analysing similar data, by sharing their approach, insights and algorithms to tackle generalized problems. The following Paper ensure to provide the methodologies used for development of the application. It provides the obtained outcomes of the features developed within the application.