COVID 19 Case Study And Predictions

Abstract

1. Introduction

Dataset consists of time-series data from 22 JAN 2020 to Till date (Updated on daily Basis).

or click Download ZIP in right panel of repository and extract it.

Prediction Preview Global Conformed Case Prediction

2. Objective

This repository is a case study, analysis and visualization of COVID-19 Pandemic spread along with prediction models.

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/tarunk04/COVID-19-CaseStudy-and-Predictions.git
cd COVID-19-CaseStudy-and-Predictions

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by tarunk04 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.