Heart Disease Analysis

Heart disease is currently the leading cause of death across the globe. It is anticipated that the development of computation methods that can predict the presence of heart disease will significantly reduce heart disease caused mortalities while early detection could lead to substantial reduction in health care costs. I am analysing the data to see the gender bifurcation for the patient having the heart diseases. As we can see that the count of male patients is double as compared to female patients. After doing the doamin research I got to know that heart disease doen’t depend on the gender of the patients as it depends upon other factors like heart rate, blood pressure, cholesterol etc. Whe I compared age factor, I found that the age is increasing the chances of heart diseases increases.

img <- readPNG("heartdisease.png")
 grid.raster(img)

Link: https://app.powerbi.com/groups/me/reports/1bca0964-e0ec-4541-8653-f5420d9d2d29/ReportSection

Retail Analysis

Our mission is to unleash potential of data and this report is an example how the raw data coming from a transactional system can be leveraged to create a beautiful interactive report that allows to get quick insights into company sales, understand sales fact relationships and come up with smarter business decisions in the end. This report was built on exemplary retails sales data. Which shows the retail business getting 75% sales from Ready wear chain and 25% sales getting from Bellings chain. Last month of the year has the highest gross profit and slaes by catagories chart shows which catagory has the highest sales.

img <- readPNG("retailanalysis.png")
 grid.raster(img)

Link: https://app.powerbi.com/groups/me/reports/7550e379-aec0-469d-86af-21a0afa02035/ReportSection

Covid-19 Cases in India

The COVID-19 pandemic is the defining global health crisis of our time and the greatest global humanitarian challenge the world has faced since World War II. The virus has spread widely, and the number of cases is rising daily as governments work to slow its spread. India has moved quickly, implementing a proactive, nationwide, lockdown, with the goal of flattening the curve and using the time to plan and resource responses adequately. My goal for this project is to visualize Covid-19 data and find out how its spreading in India. From the interactive reports, we can find which state has the highest number of confirmed cases, what is the recovery rate and death rate.

img <- readPNG("Covid-19dashboard.png")
 grid.raster(img)

Link: https://app.powerbi.com/groups/me/reports/96520eb7-8bf0-4ea9-b12c-de75886c8aea/ReportSection