Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.



[img] PDF
Restricted to Registered users only

Download (2MB)


Back in the time when the technological knowledge has bloom into the 21st century, technology has become one of the solutions that have been focused especially in using the machine learning to help the human making a better decision making. In the machine learning, there are feature engineering process where this method has evolved extensively in construction of novel features from the data provided within the goals to improvise the predictive learning performance. This process has been performed manually because it relies on the human domain knowledge as it a time�consuming factor that are used during the project of data science workflow. In this project, presence of the framework called Featuretools helps to automatically perform feature engineering a set of related tables. The open-source Python library explores the various feature construction choices based on the method known as Deep Feature Synthesis. Additionally, the deep feature synthesis stacks of multiple transformation and aggregation operation called Feature Primitives, to create features from data spread across many tables. In the other hand, the system allow user to specify domain or data specific choices to prioritize the exploration. The implementation of automation on feature generation was a success. Using the concept can perform deep feature synthesis to create new features and functions applied to one or more columns in a single table or to build new features from multiple tables. The output for the project is to obtain the recognition of utilizing automated feature engineering with features compare to the manual way for the data analysis and machine learning pipelines.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 23 Sep 2021 23:40
Last Modified: 23 Sep 2021 23:40
URI: http://utpedia.utp.edu.my/id/eprint/21727

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...