  Machine learning and deep learning play vital roles in predicting diseases in
the medical field. Machine learning algorithms are widely classified as
supervised, unsupervised, and reinforcement learning. This paper contains a
detailed description of our experimental research work in that we used a
supervised machine-learning algorithm to build our model for outbreaks of the
novel Coronavirus that has spread over the whole world and caused many deaths,
which is one of the most disastrous Pandemics in the history of the world. The
people suffered physically and economically to survive in this lockdown. This
work aims to understand better how machine learning, ensemble, and deep
learning models work and are implemented in the real dataset. In our work, we
are going to analyze the current trend or pattern of the coronavirus and then
predict the further future of the covid-19 confirmed cases or new cases by
training the past Covid-19 dataset by using the machine learning algorithm such
as Linear Regression, Polynomial Regression, K-nearest neighbor, Decision Tree,
Support Vector Machine and Random forest algorithm are used to train the model.
The decision tree and the Random Forest algorithm perform better than SVR in
this work. The performance of SVR and lasso regression are low in all
prediction areas Because the SVR is challenging to separate the data using the
hyperplane for this type of problem. So SVR mostly gives a lower performance in
this problem. Ensemble (Voting, Bagging, and Stacking) and deep learning
models(ANN) also predict well. After the prediction, we evaluated the model
using MAE, MSE, RMSE, and MAPE. This work aims to find the trend/pattern of the
covid-19.

Machine Learning