Datascience Corona Statistics

A comprehensive data science project analyzing the development of the COVID-19 pandemic using datasets provided by Statistik Austria.
Authors: Yasin Sahin and Sven Oberwalder

Table of Contents

• Project Overview
• Datasets
• Setup and Installation
• Features
• Usage
• Visualization
• Results
• License

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Project Overview

This project explores COVID-19-related statistics, focusing on vaccination, recovery, and demographic analysis. The data was cleaned, prepared, and visualized to uncover patterns and correlations.

Datasets

• Dataset 1: Contains demographic details by political district.
• Dataset 2: Includes economic and educational details by federal states.

Source:
Dataset 1
Dataset 2

Setup and Installation

1. Clone the repository:

   git clone https://github.com/Sormy23/DataScience_CoronaStatistics.git
   cd DataScience_CoronaStatistics

2. Install required libraries:

   pip install pandas numpy matplotlib seaborn

3. Ensure datasets are in the data folder.

Features

• Data Cleaning: Removed duplicates, handled null values, and mapped coded data to meaningful names.
• Data Transformation: Converted numerical, nominal, and normalized values for analysis.
• Visualizations:
  • Heatmaps
  • Pairplots
  • Boxplots and Violin Plots
  • Histograms and KDEs

Usage

Run analysis scripts in Datascience_CoronaStatistics.ipynb for detailed visualizations.

Visualization

Key graphs include:

• Correlation heatmaps to detect patterns.
• Bar charts and boxplots analyzing vaccination rates by demographics.
• KDE plots for density estimations.

Results

• Correlation between education and vaccination rates was identified.
• Economic status strongly influences vaccination likelihood.
• Age and vaccination patterns suggest demographic-specific trends.

License

This project is licensed under the MIT License.