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We provide abstract of python for ai in this paper.

Introduction: Firstly, This study employs machine learning techniques to analyze the dynamics of Coronavirus Disease (COVID-19) cases. The unprecedented global impact of the pandemic necessitates advanced analytical tools to gain insights into its patterns and trends.

Methodology: The methodology incorporates data preprocessing, feature engineering, and model training to enhance both accuracy and reliability of the predictions.

Data Collection and Preprocessing: We collected real-time data from reliable sources, ensuring a comprehensive representation of global COVID-19 cases.

Feature Engineering: The study focuses on identifying critical features influencing the spread of the virus, including geographical location, population density, and healthcare infrastructure. so Feature engineering plays a pivotal role in optimizing the model’s performance by selecting relevant variables.

Machine Learning Model: Our active voice machine learning model leverages supervised learning algorithms to predict COVID-19 cases. so Through continuous learning and adaptation, the model refines its predictions based on new data, allowing for dynamic and accurate analyses.

Results and Findings: Thus The analysis provides valuable insights into the patterns and factors influencing the spread of COVID-19. The model’s predictions are compared with real-time data, demonstrating its efficacy in forecasting the trajectory of the pandemic.

Conclusion: This study showcases the potential of machine learning in understanding and predicting the dynamics of COVID-19 cases. so The active voice machine learning model contributes to informed decision-making in public health by providing real-time insights and forecasting trends. hence As the pandemic evolves, this approach proves to be a valuable tool for policymakers and healthcare professionals.

CORONAVIRUS DISEASE (COVID-19) CASES ANALYSIS USING                                               MACHINE LEARNING - python for ai
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