Abstract
Day by day, the number of confirmed Covid-19 cases significantly increases all over the world. In India, the second wave of coronavirus has come back and created a disastrous impact. On April 3rd, India continuously recorded the highest number of daily cases globally, according to Financial Times, there was a scarcity of crematoriums and burial grounds due to the high number of corpses. The outbreak of death cases was an unprecedented circumstance, hence, there was a shortage of medical necessities. Prediction of death cases could help the government to manage the medical facilities such as beds and oxygen supply for the hospital. Machine learning could be used to analyze and predict fatality cases. PySpark library is used to process raw data and update new data each day, as the library allows the processing of a large amount of raw data efficiently. By using the Naïve Bayes algorithm available in PySpark, the prediction accuracy has increased to 81.3%.
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