2023323-MarcosOliveira / CA1

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CCT College Dublin - CA1

Data Exploration & Preparation

Lecturer Name: Dr. Muhammad Iqbal

Assessment Title: CA1 Project

Student Name: Marcos Vinicius de Oliveira

Student Number: 2023323

Submission Requirements

  • Datasets uploaded on Moodle.
  • Code named 2023323_Marcos_Oliveira_CA1.R uploaded on moodle.
  • Project report uploaded on Moodle.
  • GitHub 5 commits or more before submission.
  • Number of words added to the cover page.
  • References in HARVARD Style.
  • Upload a CCT Assessment Cover Page

Introduction

As the world suffered from the COVID-19 pandemic, the role of vaccination was crucial for stopping the spread of the virus. The European Union and European Economic Area (EU/EEA), with their collective efforts towards healthcare and disease prevention, present a unique landscape of vaccination strategies for its diverse population.

The EU/EEA, with an estimated population of over 447 million across its member countries, has been tackling the pandemic through a coordinated vaccination schema. Known for its integrated approach to healthcare, the region has witnessed varied vaccination rates and patterns that set it apart from other global entities.

In the following sections, we will be taking a look at the COVID-19 Vaccination Dataset for the EU/EEA. This investigation is crucial for understanding the region's public health response and for dissecting the vaccination progress with a data-driven approach.

Description

Problem Domain:

The problem domain of this analysis revolves around understanding and quantifying the vaccination response to COVID-19 within the European Union and European Economic Area. The goal of this analysis is to identify trends, patterns, and anomalies in vaccination rates, types of vaccines administered, and demographic specifics across different EU/EEA countries. By understanding factors such as vaccine distribution, uptake among different population groups, and the impact of vaccine refusal, this research aims to provide a comprehensive overview of the effectiveness and challenges of vaccination. Also, the study tries to explore how socio-political, economic, and geographical factors have influenced vaccination strategies and understanding of public health responses in the face of a global health crisis.

Motivation:

My motivation for doing this analysis comes from both a personal and academic perspective. On a personal level, my wife and I are considering relocating to a country in Europe in the future. Therefore, understanding the healthcare responses, particularly regarding COVID-19 vaccination strategies, in different European countries is important for our long-term health and well-being. Academically, this analysis provides an opportunity to go deeper into a significant public health topic, offering insights into how different EU/EEA countries have navigated the complexities of a large-scale health crisis. The results of this study are not just relevant for current public health strategies but also serve as a valuable resource for future pandemics and healthcare planning.

Dataset:

The data used in this study is sourced from the European Centre for Disease Prevention and Control (ECDC), an agency of the European Union dedicated to strengthening Europe's defences against infectious diseases. Specifically, the dataset focuses on a detailed record related to COVID-19 vaccination in the EU/EEA (European Union/European Economic Area) countries.

References

listBrownlee, J. (2017). Why One-Hot Encode Data in Machine Learning? [online] Machine Learning Mastery. Available at: https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/ [Accessed 29 Nov. 2023].

ECDC Europa (2021). Data on the Daily Number of New Reported COVID-19 Cases and Deaths by EU/EEA Country. [online] European Centre for Disease Prevention and Control. Available at: https://www.ecdc.europa.eu/en/publications-data/data-daily-new-cases-covid-19-eueea-country [Accessed 19 Nov. 2023].

Kaplan, J. & Schlegel, B. (2023). fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. Version 1.7.1. Available at: https://github.com/jacobkap/fastDummies, https://jacobkap.github.io/fastDummies/ [Accessed 20 Nov. 2023].

University of Toronto Libraries (2020). COVID-19 Data in R | Map and Data Library. [online] mdl.library.utoronto.ca. Available at: https://mdl.library.utoronto.ca/technology/tutorials/covid-19-data-r#cleaningdata [Accessed 20 Nov. 2023].

Villasante Soriano, P. and Kebabci, C. (2023). Draw Biplot of PCA in R (2 Examples) | biplot() & fviz_pca_biplot(). [online] Statistics Globe. Available at: https://statisticsglobe.com/biplot-pca-r [Accessed 1 Dec. 2023].

Copyright Disclaimer

Please note that this project is part of CCT College, however, it may contain some part of the code that may be copyrighted, if so, please contact me so I can delete or give due to copyright. All references about this project are in the "References" section above.

Please note this college project is non-profit and not intended to be monetized.


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