Please use this identifier to cite or link to this item:
Title: Big data analytics
Authors: Chua, Zhen Hong
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2019
Abstract: With the ease of access to connected devices and online services, data of a wide variety are constantly being collected by various service providers. These data can be used for trend-finding and the prediction of future values, such outcomes having an importance in optimization for a variety of industrial, commercial and even consumer processes. With the widespread availability of highly capable computing systems and programming tools, resource-intensive tasks like the implementation of predictive machine learning is now possible at low cost for a determined user. The objective of this project is to produce a machine learning-based process, capable of predicting a numerical output based upon a set of mixed-type input data. This process is implemented with open-source programming tools. Furthermore, the project also seeks to predict the relative importance of the different data features. In this project, we have developed a machine-learning process capable of predicting a numerical output with up to 0.77 explained variance. The process encompasses the entire data analysis procedure, from data importation, data pre-processing, hyperparameter optimization and prediction. The process developed shows that a functional, and reasonably accurate data analysis model, can be produced using open-source software. Using a variety of machine-learning algorithms, the project also shows the relative accuracy of, and time taken by the different models in producing a predicted output.
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
835.72 kBAdobe PDFView/Open

Page view(s)

Updated on Jun 15, 2024


Updated on Jun 15, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.