COVID-19: A Data-Driven Analysis of Cases and Deaths Worldwide

Overview

This project analyzes the global impact of the COVID-19 pandemic using data science techniques, focusing on cases and deaths worldwide. The analysis includes regression models and visualizations to gain insights into the spread and impact of the pandemic.

Dataset

The dataset used in this project is available on Kaggle: COVID-19 Cases and Deaths Worldwide

Features of the Dataset:

• Total cases, deaths, recoveries, and active cases
• Population and continent-level data
• Critical case statistics

Objectives

• Perform descriptive statistical analysis.
• Analyze the relationship between COVID-19 cases and deaths using regression models.
• Identify trends and patterns in the pandemic’s global impact.

Files

• analysis/We R.Rmd: R Markdown file containing the analysis and regression modeling.
• report/FINAL REPORT DSC 423- TEAM (WE R).pdf: Final report summarizing the methodology, results, and conclusions.
• data/: Placeholder for dataset information (link or instructions to download the dataset).

Results

• Linear regression models provided insights into the relationship between cases and deaths.
• Visualizations highlight disparities in COVID-19 impacts across regions.

Future Work

• Long-term analysis of pandemic effects.
• Exploring the impact of vaccination campaigns.
• Analyzing risk factors such as age, comorbidities, and socioeconomic conditions.

Authors

• Tejas Gotiwale
• Megha Kaavali Mahhadevaappa
• Vinila Kusuma
• Dikshit Pabathi
• Digvijay

References

• Kaggle COVID-19 Cases and Deaths Worldwide dataset: Link
• World Health Organization: COVID-19 Dashboard
• Johns Hopkins University: COVID-19 Data Repository