CIÊNCIA DE DADOS APLICADO À LOGÍSTICA

Lucas Confortini Batista; Marcus Rogério de Oliveira

doi:10.31510/infa.v19i1.1397

Authors

Lucas Confortini Batista Faculdade de Tecnologia de Taquaritinga (Fatec) – Taquaritinga – SP – Brasil https://orcid.org/0000-0002-1734-8930
Marcus Rogério de Oliveira Faculdade de Tecnologia de Taquaritinga (Fatec) – Taquaritinga – SP – Brasil https://orcid.org/0000-0002-8823-1916

DOI:

https://doi.org/10.31510/infa.v19i1.1397

Keywords:

Data, Science, Regression, Logistics, Prediction

Abstract

The amount of data grows exponentially and it is important that knowledge and information can be abstracted from them, in order to generate competitive advantages. In view of this, data science emerged with the aim of extracting this information and interpreting it with mathematical, statistical models and artificial intelligence algorithms. In this way, this article, with the use of mathematical and statistical models, aims to apply regression models in logistics, in order to obtain cost prediction. Within this context, this article presents the performance of data science applied to the area of logistics, with an introduction to terms, methods and practical experience, emphasizing the partition of cost prediction. The proposed methodology initially covers a descriptive bibliographic research and later, analyzes more than 30 thousand coefficients applied to different regression models. The results allow identifying the influence of coefficients on net income and comparing the accuracy of regression models applied to cost prediction.

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References

Nelli Fabio. Python Data Analytics, Data Analysis and Science UsingPandas, matplotlib, and the Python Programming Language. APRESS.

Guerra Saulo, Oliveira Paulo Felipe e McDonnell Robert. Ciência de Dados com R Introdução.

Provost Foster, Fawcett Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking.

Steele Brian, Chandler John e Reddy Swarna. Algorithms for Data Science.

Mailund Thomas. Beginning Data Science in R Data Analysis, Visualization, and Modelling for the Data Scientist.

Manyika James, Chui Michael, Brown Brad, Bughin Jacques, Dobbs Richard, Roxburgh Charles, e Hung Byers Angela. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute

Gorelik Alex. The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science. O'Reilly, 2019.

Skiena S. Steven . The Data Science Design Manual. Springer, 2017. DOI: https://doi.org/10.1007/978-3-319-55444-0

EMC Education Services. Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. WILEY, 2015. DOI: https://doi.org/10.1002/9781119183686

Provost Foster e Fawcett Tom. Data Science for Business What you need to know about Data Mining and Data-Analytic Thinking. O'REILLY, 2013.

Kukreja Manoj. Data Engineering with Apache Spark, Delta Lake, and Lakehouse: Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way. Pack, 2021.

Crickard Paul. Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python. Packt, 2020.

VanderPlas J. Python Data Science Handbook. O'REILLY, 2017.

Apresenta a documentação das bibliotecas do Python. Disponível em: <https://docs.python.org/3/library/index.html>. Acesso em 22 fevereiro de 2022

Apresenta a documentação da extensão SciPy. Disponível em: <https://docs.scipy.org/doc/scipy/tutorial/general.html>. Acesso em 22 fevereiro de 2022

Müller C. Andreas e Guido Sarah. Introduction to Machine Learning with Python. O’REILLY, 2017.

Apresenta a documentação do XGBoost. Disponível em: <https://xgboost.readthedocs.io/en/stable/index.html>. Acesso em 22 fevereiro de 2022

Apresenta a documentação do LightGBM. Disponível em: <https://lightgbm.readthedocs.io/en/latest/index.html>. Acesso em 22 fevereiro de 2022

Nokeri C. Tshepo. Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn. APRESS, 2022. DOI: https://doi.org/10.1007/978-1-4842-7762-1

Minastireanu E. A. e Mesnita G. .Light GBM Machine Learning Algorithm to Online Click Fraud Detection. IBIMA, 2019. DOI: https://doi.org/10.5171/2019.263928

Densmore James. Pipelines Pocket Reference Moving and Processing Data for Analytics. O’REILLY, 2021.

Zhang Arthur. Data Analytics Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life. 2017.

Kuhn Max e Johnson Kjell. Applied Predictive Modeling. Springer, 2013. DOI: https://doi.org/10.1007/978-1-4614-6849-3

Zinoviev Dmitry. Data Science Essentials in Python Collect → Organize → Explore → Predict → Value. The Pragmatic Programmers, LLC, 2016.