Mona Swarnakar

  I have only worked on Power BI but to use this voronoi treemap, I have chosen Tableau. I have mentioned the link below, if anybody would like to work on this. Please share your feedback, if you like it. :-) Veronoi treemap link: https://lnkd.in/gVqFuZ8M Thanks for reading.

The goal of the project to find out the top KPIs of the company. I have categorized my dashboard report into four category: Products : Which products are the top revenue generating? Sales : What is the sales trend of the company over the last three years? Customers : Who are the customers? Which countries or region has highest revenue or more number of customers? Shipping : Who is the top shipper? what is the average time of delivery? which coutries are paying more freight charges than generating revenue? Through this above points, you can find out the top areas where company is doing great and where the company needs to improve. Tool Used : Power BI

  About this dataset: I have considered year 2019 as analysis year (as data after that is incomplete) and 2015 as base year (for comparison). Also, i have categorized different type of waste into five category: plastic, cigarette, metal and glass, ghost fishing and others to understand in better way. I have added one table to show about the per capita waste generations by sates, U.S in 2019. Hope rest you can understand. Tool Used: Power BI

  Do you want to know, which are the top cities in the world are most and least livable? If yes…please find the below Visualization. I have used annual rankings from the Economist Intelligence Unit (EIU) to show the world’s most livable cities, measuring different categories including: stability, healthcare, culture and environment, education, and infrastructure. Source:  https://lnkd.in/gh5q4yEK Key Insights: • Vienna (Austria) tops the rankings in 2022, as it did in 2019 and 2018. • Russia’s invasion of Ukraine on February 24th has forced to exclude Kiev (Ukraine) from survey. • Eastern European cities slip in the rankings amid increased geopolitical risks. • Western European and Canadian cities dominate the top of our rankings. Life is almost back to normal in these cities on account of high covid-19 vaccination rates and the easing of restrictions. • Damascus (Syria) and Tripoli (Libya) continue to languish at the bottom of the list—along with Lagos (Nigeria)—as they face ...

I have seen Word-cloud in so many places such as magazines, websites, blogs, etc. however never thought of making it by myself. I would like to thank R that taught me such a wonderful technique which everybody would like to learn. I don’t know if there are any other free options are available to create word-cloud. Let me give a quick explanation about R first, R is a free source packages and very useful for statistical analysis. We can use R for various purposes, from data mining to data visualization. Word-cloud is a tool where you can highlight the words which have been used the most in quick visualization. If you know the correct procedure and usage, then Word Cloud is simple in R Studio. Further, a package called “Word-cloud” is released in R, which will help us to create word-cloud. You can follow my simple four steps mentioned below to create word-cloud. Those are new to R or Word Cloud, I would suggest first install R studio from the link rstudio.com Also, the following...

To find this presentation, click below link: http://www.slideshare.net/monaswarnakar/trendin-company-analysis Description:  Analysis of Trendin company: The report identifies major changes/additions required by Trendin.com and how the company can improve its revenue as compare to other e-retail companies such as Myntra.com, Flipkart.com, Zovi.com, etc. Further, the report focuses on how Trendin can change its Home page to attract more customers and to increase its visibility on search results. The report also has a SWOT analysis of Trendin. 

Assignment:4 Assignment 4 deadline got over today. So i am putting the assignment 4 questions and answers here. Hope it will help others to solve the questions.  setwd("  ") library(psych) PE=read.table("Stats1.13.HW.04.txt",header = T) describe(PE) #Question1: What is the correlation between salary and years of professional experience? cor(PE[2:4]) round(cor(PE[2:4]),2)            salary     years   courses salary  1.0000000 0.7448961 0.5410249 years   0.7448961 1.0000000 0.3336635 courses 0.5410249 0.3336635 1.0000000 > round(cor(PE[2:4]),2)         salary years courses salary    1.00  0.74    0.54 years     0.74  1.00    0.33 courses   0.54  0.33    1.00 #Question2: What is the correlation between salary and courses completed? round(cor(PE$salary, PE$courses),2) [1] 0.54 ...