COVID-19 Predictive Analysis using Supervised and Ensemble Learning Models

This repository presents a predictive analysis project focused on COVID-19 using supervised and ensemble learning models. By harnessing Python libraries including pandas, scikit-learn, seaborn, and matplotlib, this study aims to improve COVID-19 classification accuracy and contribute to early illness detection.

Introduction

This project entails the application of supervised and ensemble learning models to predict COVID-19 outcomes. The analysis involves several key steps, including data preprocessing, model selection, training, evaluation, and performance comparison.

Results and Conclusion

The classification model demonstrated promising results, though there is room for refinement, especially in reducing false positives and false negatives. Notably, the model achieved an impressive maximum F1 score of 0.98 and an accuracy of 98.0%. Among classifiers, Support Vector Machine (SVC) and Random Forest exhibited superior accuracy and F1 scores, with SVC showcasing zero false negatives – a critical factor for COVID-19 diagnosis.

Project Details

Dataset: Utilized COVID-19 data for predictive analysis. Skills Used: Employed Python for data manipulation, scikit-learn for model implementation, seaborn and matplotlib for visualization. Models: Explored various supervised and ensemble learning models for COVID-19 prediction. Methods: Utilized confusion matrix, F1 score, and accuracy for model evaluation. Contributions: Contributions are welcomed through pull requests, fostering collaborative enhancement. Usage

To engage with this repository:

Clone the repository to your local environment. Use Python to execute scripts, employing libraries as required. Review and adapt the provided codebase for your specific analysis or research.

Future Prospects

This work lays the groundwork for further advancements in COVID-19 prediction models. Future iterations could involve crafting new algorithms based on existing models to improve early illness detection. Enhanced data quality can be achieved by aggregating information from diverse sources, including health organizations, social media, various ethnicities, and geographical regions, ensuring a well-rounded approach.