A Practical Guide to Support Predictive Tasks in Data Science

José Filho, José Monteiro, César Mattos, Juvêncio Nobre

2021

Abstract

Currently, professionals from the most diverse areas of knowledge need to explore their data repositories in order to extract knowledge and create new products or services. Several tools have been proposed in order to facilitate the tasks involved in the Data Science lifecycle. However, such tools require their users to have specific (and deep) knowledge in different areas of Computing and Statistics, making their use practically unfeasible for non-specialist professionals in data science. In this paper, we propose a guideline to support predictive tasks in data science. In addition to being useful for non-experts in Data Science, the proposed guideline can support data scientists, data engineers or programmers which are starting to deal with predictive tasks. Besides, we present a tool, called DSAdvisor, which follows the stages of the proposed guideline. DSAdvisor aims to encourage non-expert users to build machine learning models to solve predictive tasks, extracting knowledge from their own data repositories. More specifically, DSAdvisor guides these professionals in predictive tasks involving regression and classification.

Paper Citation

in Harvard Style

Filho J., Monteiro J., Mattos C. and Nobre J. (2021). A Practical Guide to Support Predictive Tasks in Data Science. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 248-255. DOI: 10.5220/0010460202480255

in Bibtex Style

@conference{iceis21,
author={José Filho and José Monteiro and César Mattos and Juvêncio Nobre},
title={A Practical Guide to Support Predictive Tasks in Data Science},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={248-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010460202480255},
isbn={978-989-758-509-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Practical Guide to Support Predictive Tasks in Data Science
SN - 978-989-758-509-8
AU - Filho J.
AU - Monteiro J.
AU - Mattos C.
AU - Nobre J.
PY - 2021
SP - 248
EP - 255
DO - 10.5220/0010460202480255