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https://github.com/henrylin03/covid-nsw
This project includes an interactive Streamlit dashboard that explores and visualises COVID's impacts in NSW, Australia.
https://github.com/henrylin03/covid-nsw
analysis coronavirus covid-19 covid19-data data-analysis data-science data-visualization matplotlib pandas pandas-python python python3 seaborn sql streamlit streamlit-dashboard visualization
Last synced: 6 days ago
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This project includes an interactive Streamlit dashboard that explores and visualises COVID's impacts in NSW, Australia.
- Host: GitHub
- URL: https://github.com/henrylin03/covid-nsw
- Owner: henrylin03
- Created: 2021-10-10T05:12:30.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-24T01:33:16.000Z (over 1 year ago)
- Last Synced: 2023-07-24T03:10:19.814Z (over 1 year ago)
- Topics: analysis, coronavirus, covid-19, covid19-data, data-analysis, data-science, data-visualization, matplotlib, pandas, pandas-python, python, python3, seaborn, sql, streamlit, streamlit-dashboard, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 76.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# COVID-19 in NSW: Dashboard
This is a Python dashboard visualising Covid's impact on New South Wales (NSW), Australia. Built on the [Streamlit](https://streamlit.io/) library, this dashboard allows users to interactively explore daily, reported cases from [NSW Health](https://data.nsw.gov.au/search/dataset/ds-nsw-ckan-aefcde60-3b0c-4bc0-9af1-6fe652944ec2/details?q=).
## Project Overview
This project includes:
- **Interactive Dashboard**: The interactive `streamlit` dashboard is located in `dashboard.py`. It allows users to explore NSW's COVID-19 data, including adjusting visualisation parameters to gain insights.
- **Data Pipeline Building**: The data pipeline is built in `pandas`, automating data fetching, cleaning, and analysis.
- **Data Cleaning & Preparation**: The raw data is cleaned and transformed to remove duplicates, missing values, and other inconsistencies that would impact analyses.
- **Data Exploration & Analysis**: Exploratory data analysis (EDA) is done in a Jupyter notebook using SQL and Python libraries such as `pandas`, `seaborn` and `matplotlib`. The notebook is included in this repository as `analysis.ipynb`.
- **Data Visualisations**: The dashboard includes `seaborn` and `matplotlib` visualisations to highlight key patterns.
## Technologies Used
- **`streamlit`**: for building the interactive dashboard
- **`seaborn` & `matplotlib`**: for data visualisation and exploration
- **`pandas`**: for data cleaning, preparation, and analysis
- **`wikipedia`** : for data fetching using Wikipedia API
- **SQL (`sqlite`)**: for data cleaning, preparation, and manipulation
## How To Use
Access the dashboard via [covid-nsw.streamlit.app](https://covid-nsw.streamlit.app/).
To replicate my EDA, please open and run the `analysis.ipynb` Jupyter notebook to see the results of the analysis:
```bash
jupyter notebook
```
## Conclusion
Through this project, I have expanded my skills in building interactive data dashboards, including building its composite data visualisations, data transformations, and data pipeline.
Please feel free to raise a [GitHub Issue](https://github.com/henrylin03/covid-nsw/issues) if you have any questions or feedback. Thank you!