Voting Classification Approach for Covid-19 Prediction with K-mean and PCA

Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers.