{'fraction_non_alphanumeric': 0.02585551330798479, 'fraction_numerical': 0.009125475285171103, 'mean_word_length': 4.9818181818181815, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
PREDICTION OF COVID-19 USING SOME MACHINE LEARNING MODELS AND ITS COMPARISON WITH A DEEP LEARNING MODEL

Coronavirus (COVID-19) started from Wuhan, China in December 2019. Since then, this virus has affected millions of people around the world and has caused deaths in millions. As of right now, there is no cure or permanent treatment for this disease. It is well known that machine learning plays an important role in the health care system.
In this research, we are going to use some machine learning models such as Decision Tree (DT), logistic regression (LR), and Random Forest (RF) for the forecasting of corona virus. These models are implemented using different machine learning libraries available in Python. This work not only serves the purpose of COVID-19 predictions using machine learning, but also attempts to find out suitable model with the best features to save time and resources. Furthermore, we also compare some of the features of different machine learning models with a deep learning model (CNN). Since healthcare environment, computational resources have to be optimized, prediction models which use less computational resources are always preferred. We believe that the outcomes of this study can help understand the performance of various predictions models in the prediction of COVID-19.