• Source: http://www.ign.com/reviews/games
• Pretty clean data
• Packages Used: ggplot2, dplyr, grid

• Source: https://www.kaggle.com/jboysen/injured-workers
• Packages Used: highcharter, plotly
• Acknowledgement: https://www.osha.gov/severeinjury/index.html
• Blog link: https://ashishtele1992.wordpress.com/2018/05/15/the-dangerous-places-to-work-in-the-usa/

• Source: https://www.kaggle.com/START-UMD/gtd
• Data from 1970 to 2016
• Packages Used for visualization: ggplot2, highcharter
• Packages used for text analysis: tm, tidytext
• Analysis is focused on India
• Acknowledgment: http://start.umd.edu/gtd/about/GTDTeam.aspx

• Source: https://www.kaggle.com/caicell/zomato-india-restaurants-eda
• Zomato Restaurants data
• Data analysis for strategic implementation (India)
• Still working on the dataset

• Source: http://archive.ics.uci.edu/ml/datasets.html?task=cla&area=&type=&view=list
• Gender classification
• Tweet text analysis (WIP)

• Source: https://takeout.google.com/settings/takeout?pli=1
• Data collected from my devices
• Blog link 1: https://ashishtele1992.wordpress.com/google-knows-more-about-you-than-you-think-part-1/
• Blog link 2: https://ashishtele1992.wordpress.com/google-knows-more-about-you-than-you-think-part-2/

• Source: https://volcano.si.edu/list_volcano_holocene.cfm
• Acknowledgement: http://www.makeovermonday.co.uk/data/
• Makeover Monday (week 27)

• Source: https://www.whatsthecap.com/nba/salary-cap/
• Acknowledgement: http://www.makeovermonday.co.uk/data/
• Makeover Monday (week 29)

• Source: https://volcano.si.edu/list_volcano_holocene.cfm
• Acknowledgement: http://www.makeovermonday.co.uk/data/
• Make over Monday (week 28)

• Source: https://www.kaggle.com/payel58/dataset-for-hotels-in-indian-cities
• Packages used: dtplyr, ggplot etc
• Interactive Power BI report for quick analysis

• Packages used: forecast, dplyr, Xgboost, etc.

Thank you!!