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Introduction: In the relentless battle against the COVID-19 pandemic, predicting its future trajectory is crucial for effective decision-making and resource allocation. This abstract explores the utilization of models for forecasting COVID-19 trends.

Methodology: Under the supervised machine learning paradigm, historical COVID-19 data, including infection rates, mortality, and recovery rates. so Feature engineering and data preprocessing will enhance the model’s ability to capture intricate patterns in the data.

Model Selection: Various algorithms, such as Support Vector Machines (SVM), Random Forest, and Neural Networks, will be explored for their efficacy in forecasting COVID-19 trends. Model selection criteria will consider factors like accuracy, precision, and recall.

Results and Findings: so The active application of these models to real-time data will provide a dynamic forecasting tool. Preliminary results indicate promising accuracy rates, showcasing the potential of supervised machine learning in predicting COVID-19 future trends.

Significance: Hence By harnessing the power of machine learning, this research contributes to a more data-driven and proactive approach to pandemic management. The timely identification of potential hotspots and accurate forecasting will aid healthcare systems, governments, and communities in making informed decisions.

Conclusion: In conclusion, the integration of supervised machine learning models in COVID-19 forecasting offers a promising avenue for improving preparedness and response strategies. As the pandemic continues to evolve, leveraging these advanced technologies can be a cornerstone in our collective efforts to mitigate its impact and protect global public health.

Show 2 Comments


  1. teja

    which algorithms have been used to develop this project

    • four standard forecasting models, such as linear regression (LR), least absolute shrinkage and selection
      operator (LASSO), support vector machine (SVM), and exponential smoothing (ES)

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