Predictive Analytics Applications with WEKA

Shuzlina Abdul Rahman & Sofianita Mutalib

2021. g. janv. ·

Par šo e-grāmatu

This module offers a simple way yet interesting approach in applying data mining tools such as Waikato Environment for Knowledge Analysis (WEKA), an open source machine learning software. The practical hands-on of the tools and techniques for machine learning used in data mining is described step-by-step in five sub-modules. For each sub-module, a description about the topic is given for a better understanding. Inside, you'll learn about preparing the data, data cleaning, modelling, and results evaluation. The module ends by providing a check-list activity and common error that you may encounter. Three case studies are demonstrated from different sources of dataset using the features offered in WEKA. The module would be a good source for hands-on-introduction to machine learning algorithms with no extensive background in mathematic required. Predictive Analytics Applications with WEKA is an accessible introduction to this rapidly growing industry and suit for any students and researchers looking for a simple predictive analytics exercise.

Par autoru

SHUZLINA ABDUL RAHMAN is currently an Associate Professor at the Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA (UiTM) Shah Alam, MALAYSIA. She obtained her Bachelor degree in Computer Science majoring in Information Systems from the Universiti Sains Malaysia (USM). She worked at Mediquip Sdn. Bhd. as a System Analyst, and later pursued her studies at Universiti Utara Malaysia (UUM) and completed her Master degree in Information Technology. She started her career as a lecturer at UUM before joining UiTM and pursued her PhD in System Management and Science from the Universiti Kebangsaan Malaysia (UKM). She was awarded as the recipient of the prestigious MIMOS 2012, the highest recognition of the contributions of the academia to the nation’s innovation to the outstanding achievements of doctoral-level researchers in the areas of Information Communications and Technology (ICT). She has been teaching and actively doing research in the area of data mining & optimization, intelligent data analytics and soft computing. Being an academician for the last 20 years has motivated her to write this module that could benefit students and researchers in the field.

SOFIANITA MUTALIB is currently a senior lecturer, with PhD, at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Shah Alam, MALAYSIA. She started her career in education after she obtained her Bachelor degree and Master in Information Technology from the Universiti Kebangsaan Malaysia (UKM). She worked at Universiti Tun Abdul Razak (UNITAR) for almost three years. She later joined UiTM to teach Bachelor degree in Intelligent Systems Engineering (formerly known as Intelligent Systems). She had expanded her career by finishing her PhD degree at UiTM in the area of data mining and specifically with bioinformatics field. She is now involved in undergraduate and postgraduate programmes for teaching, research and supervision. She has been teaching Data Mining for the last 15 years with keen interest and passion for research works in applied data mining towards data sciences.