ABSTRACT
Due to the Covid-19 pandemic, hospitality industry witnessed a massive decline in their revenues. In our research we realized that one of the most effective ways to aid customer retention and boost the revenue of this Our research shows that currently the data analysts in this industry only use the traditional tools for predictive analysis, offering from a limited range of offers that lack customization as per user purchase history. Hence, we put forward a proof of concept for a tool where we make a machine learning model that learns from the historic data of each restaurant, including customer segments and coupon parameters, and predicts the probability of a coupon to work on a specific sub-category of customers. This would thereby increase the chances of transaction and thus boost the revenue. We worked with several classification algorithms, like Logistic Regression, AdaBoost, Random Forest, Gradient Boosting, and realized that Random Forest Classifier was producing the best results. Thus we selected it for building our model. As a result, we have built a web-based tool that can be used by Analysts or the business person themselves, to find out what coupon offers would best suit a particular subset of customers. This would help them make better business decisions, gain more customer traction and retention, and consequently boost their revenue. © 2022 IEEE.
ABSTRACT
OBJECTIVES: Coronaviruses are globally emerging viruses that threaten our health care systems and have become a popular pandemic around the world. This causes a sudden rise in positive coronavirus cases and related deaths to occur worldwide, representing a significant health hazard to humans and the economy. METHODS: We examined predominantly catechins of green tea include epigallocatechin-3-gallate (EGCG), epicatechin-3-gallate (ECG), and drugs of chloroquine (CQ), and hydroxychloroquine (HCQ) appearing to reveal anti-viral activities. Data were collected from PubMed, Google Scholar, and Science Direct databases. To investigate the role of antiviral effects (CQ and HCQ), green tea catechins, beneficial use of convalescent plasma; covaxin in COVID-19 patients faced a dangerous healthiness issue. Computational docking analysis has been used for this purpose. RESULTS: The lead compounds are EGCG and ECG act as potential inhibitors bind to the active site region of the HKU4-CoV 3CL protease and M-Pro protease enzymes of coronavirus. Conclusions: SARS-COV-2 is a pathogen of substantial vigour concern and the review unveils the role of catechins associated with many viral diseases. We suggested that the function of green tea catechins, novel drugs of CQ, and HCQ exhibit antiviral activities against positive-sense single-stranded RNA viruses (CoVs).