Nowadays Covid-19 is prevailing across the world, it has affected millions of populations across the world. The exponential increase in covid cases makes the health care system overwhelmed. Many testing methods are used for covid-19 detection like Rapid antigen test, RT-PCR test, etc. These tests have certain limitations, sometimes people got confused between respiratory infection and covid-19infection, as many symptoms are similar. So for confirming the disease, a chest x-ray is preferred. Covid-19 has similar symptoms of pneumonia, consolidation, and ground-glass opacities, in our approach we consider them as covid. In this paper, images are acquired from reputed hospitals and various online datasets used in Covidnet architecture. After accumulation, the dataset is verified by experienced radiologists. In our approach, we trained our models on various symptoms of covid19 like pneumonia, consolidation, ggopacities and finally on covid-19 dataset images. In our research, we have used single as well as ensemble models for classification. Models like densenet, efficient net, resnet, etc are used. Certain preprocessing techniques are used before passing the image dataset into training like adaptive histogram equalization, data augmentation methods, etc. Finally, a approach based on Deep Learning used for identification of covid 19. We are claiming 95% plus testing accuracy and 99% training accuracy. Beyond classification, we further generate the reports and localize the covid virus on Xray using various visualization methods. Further results are classified based on single and ensemble models on the in-house dataset.