Analysis and Prediction of COVID-19 Positive Cases using Machine Learning Models in Maghrebian Countries

As the world grapples with the ongoing COVID-19 pandemic, machine learning models have emerged as indispensable tools for predicting and analyzing positive cases. Through their ability to process vast amounts of data and identify complex patterns, these models offer valuable insights to guide public health strategies. However, challenges such as data quality, ethical considerations, and integration into existing frameworks remain. As researchers continue to refine and optimize machine learning models, their potential to aid in the fight against COVID-19 becomes increasingly promising. By leveraging the power of artificial intelligence, we can better anticipate the course of the pandemic, allocate resources efficiently, and ultimately save lives. This work is a simple Comparing different machine learning techniques (Random Forest RF, LSTM, ARIMA, Support Vector Machine SVM and extreme Gradient Boosting XGBoost) for predicting the number of COVID-19 positive cases, fatalities, and individuals who have received the vaccine. The best-performing model that gives motivating results can be used for the prediction of any epidemic disease. This study can help researchers analyze and predict COVID-19 in Maghrebian Countries.