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https://github.com/akansharajput280799/covid19-impact-analysis-usa
Data Analysis and Predictive Modeling to study COVID-19 impact across age groups, regions, and seasons in the USA.
https://github.com/akansharajput280799/covid19-impact-analysis-usa
classification-algorithm clustering-algorithm data-preprocessing data-visualization descriptive-statistics exploratory-data-analysis matplotlib numpy pandas seaborn
Last synced: 21 days ago
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Data Analysis and Predictive Modeling to study COVID-19 impact across age groups, regions, and seasons in the USA.
- Host: GitHub
- URL: https://github.com/akansharajput280799/covid19-impact-analysis-usa
- Owner: AkanshaRajput280799
- Created: 2024-10-14T19:18:02.000Z (25 days ago)
- Default Branch: main
- Last Pushed: 2024-10-15T12:08:16.000Z (24 days ago)
- Last Synced: 2024-10-16T21:46:19.001Z (23 days ago)
- Topics: classification-algorithm, clustering-algorithm, data-preprocessing, data-visualization, descriptive-statistics, exploratory-data-analysis, matplotlib, numpy, pandas, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.14 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# COVID19-Impact-Analysis-USA
Data Analysis and Predictive Modeling to study COVID-19 impact across age groups, regions, and seasons in the USA.
[
](https://www.google.co.uk/url?sa=i&url=https%3A%2F%2Ftimesofindia.indiatimes.com%2Ftravel%2Fthings-to-do%2Fusa-may-need-to-extend-social-distancing-till-2022-to-beat-coronavirus%2Farticleshow%2F75166302.cms&psig=AOvVaw0FCqBDknwub2FSRxg0NIEn&ust=1729039420565000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCKiou4uUj4kDFQAAAAAdAAAAABAg)
# Project Overview
The COVID-19 outbreak placed immense strain on the U.S. healthcare system, already burdened by workforce shortages. This study addresses challenges related to pre-existing health conditions, age groups, geographic regions, and demographics, aiming to enhance preparedness for future pandemics. Using data from 2020-2023 from the U.S. Department of Health and Human Services, machine learning techniques like Decision Trees, Linear Regression, K-Means clustering, Naïve Bayes, and DBSCAN were applied. Key findings show that older populations and those with pre-existing conditions were most affected, with significant geographic and seasonal patterns. The study underscores the need for adaptive health policies to safeguard vulnerable groups and enhance pandemic readiness, offering valuable insights for future health strategies.
# Overview of Features in COVID-19 Analysis Dataset
# Technologies Used
-**Excel**:for data cleaning, descriptive statistics, data analysis
- **Python**: for data analysis and machine learning
- **Libraries**: Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn
- **Jupyter Notebooks**: for implementing models
- **Tableau**: for visualization
# Dataset
The dataset was obtained from the U.S. Department of Health and Human Services. You can find the dataset https://catalog.data.gov/dataset/conditions-contributing-to-deaths-involving-coronavirus-disease-2019-covid-19-by-age-group .
# Key Findings
- **Vulnerability of Older Adults:** Individuals over the age of 85, particularly those with pre-existing conditions like respiratory diseases or diabetes, showed significantly higher mortality rates from COVID-19.
- **Geographic Disparities:** Significant geographic inequalities were observed, with states such as New York, Texas, and California reporting notably higher COVID-19 mortality rates, while states like Alaska and Wyoming had much lower rates.
- **Seasonal Trends:** The analysis indicated a higher frequency of fatalities during the winter months, suggesting a seasonal pattern in COVID-19 transmission and mortality.
# Visual Analysis: COVID-19 Impact Dashboard
Tableau link: https://public.tableau.com/app/profile/akansha.rajput8373/viz/Covid-19USdata-AkanshaRajput/COVID-19USData