COVID-19 Severity Tracking: A data science project designed to predict the severity of COVID-19 outbreaks, enabling enhanced preventive actions. These ideas was proposed and polished in Y2 data analytics course presentation.
This project focuses on predicting severe COVID-19 outbreaks by analyzing key data patterns. The ultimate goal is to forecast specific days or areas within a month that are likely to experience heightened severity, enabling stakeholders to take preventive measures in advance.
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Data-Driven Predictions:
- Uses historical COVID-19 data, including infection rates, mobility data, vaccination coverage, and public health metrics.
- Machine learning models predict high-severity days or locations.
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Visualization:
- Heatmaps and time-series graphs highlight severity trends across regions and timeframes.
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Preventive Action Recommendations:
- Outputs actionable insights for governments, healthcare providers, and communities to better allocate resources.
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Data Preprocessing:
- Cleaning and normalizing datasets for consistent input.
- Feature engineering to include variables such as daily new cases, test positivity rates, and vaccination data.
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Modeling:
- Algorithms such as Random Forest, LSTM, or Gradient Boosting are applied to forecast future severity.
- Models are fine-tuned for high accuracy and robustness.
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Technologies Used:
- Python, Pandas, NumPy, Matplotlib, and Scikit-learn for data processing and modeling.
- Streamlit for creating an interactive dashboard for presentation