Analysis of AI-driven tools and Impact of Covid-19 on Age: A Case Study for India

The primary objective of present study was to study the impact of Covid-19 pandemic on the different age groups in Indian population. The paper has recorded observations by applying major supervised and unsupervised learning models on Covid-19 instances in India for finding a suitable model that fits the Covid-19 data set of confirmed, recovered death cases (CC, RC and DC respectively) as well as have analyzed the impact of Covid-19 on various age groups in Indian population. The total number of confirmed, recovered cases and death cases in India stand at 11.4million,11million and 1,59,000 respectively as on 15th March 2021. The RMSE obtained for linear regression is 8702 and for Polynomial regression is 1205 as far as confirmed cases are concerned. The k-means Clustering and Hierarchical Clustering identifies 4 major clusters for age groups i.e. cluster 0 for 20 years to 60 years (most impacted), cluster 1 for 0 to 20 years of age (least impacted), cluster 2 for 70 years to 120 years (least impacted), and cluster 3 for 60 years to 70 years (least impacted). The polynomial regression model with degree 4 is the best-fit model for the available instances as compared to Linear regression and Multilinear regression model. The most impacted age group for the given instances lies between 20 to 60 years whereas the least impacted age group is between 0–20 and 70 to 120 years as per the available instances.