Review of Various Applications of Machine Learning
The usage of machine learning proposes an intelligent diagnostic study program that supports a web-based learning model aiming to develop students' ability to integrate information by allowing them to select study topics of interest, find information on those topics by searching online for related reading courseware and discussing what they have learned with their peers. The suggested learning program can effectively help students improve their knowledge while browsing online using the "webbased learning" approach, based on our test results. This study, on the other hand, proposes to use a machine-learning algorithm to anticipate future stock prices by combining open source libraries with pre-existing algorithms to help make this uncertain business model predictable. The result is entirely dependent on numbers and is predicted by many assumptions that may or may not occur in the real world, such as the forecast period. At the same time, the study also aims to provide a tool to anticipate accurate and timely traffic data. This fact has prompted us to pursue a solution to the problem of predicting traffic flow based on traffic data and models. Due to a large amount of available data for the transport system, it is difficult to accurately predict traffic flow.
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 Chun-Hsin Wu, Jan-Ming Ho, and D. T. Lee. Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5(4):276–281, Dec 2004.
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 Yiming He, Mashrur Chowdhury, Yongchang Ma, and Pierluigi Pisu. Merging mobility and energy vision with hybrid electric vehicles and vehicle infrastructure integration. Energy Policy, 41:599–609, 2012.
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