Long-tailed distribution examples - Data Science Week 4

Banking : No of ATM available by city/town/Village name within Tamil Nadu
Insurance : No of people taken life insurance by city/branch name
Education : No of schools in India by city name
Healthcare : No of people treated in all Govt hospital by disease name.
Government : Population by Indian cities.
Retail : No of item purchased by consumer on weekend given by item name.
Sports : No of games played by player/Team by stadium/Ground name.
Agriculture : No of farms growing rice in India by district/city name
Automobile : No of cars registered in Chennai city by car model

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Banking : No of home loan taken branch-wise
Insurance : No of car insurance policies sold city-wise
Education : No of teachers at government schools city wise
Healthcare : No of patients affected based on disease
Government : No of RTO office city wise
Retail : No of soft drinks bottles sold based on the brand globally
Sports : No of innings played by all cricket players historically
Agriculture : No of organic farms village-wise in india
Automobile : No of cars sold based on the car brand globally

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Banking:

Long tailed – Count of employees applying loan from various locations
Uniform – No. of people visits on business day of week(Mon-Fri)

Agriculture:

Long tailed – Number of farmers using different fertilizers in a city.
Uniform – Yield in different quarters in a year.

Healthcare:

Longtailed – No. of patients coming to the hospital for different diagnosis.
Uniform – No. of visits to the hospital in every day of week.

Education:

Longtailed – No. of students who are settled in different locations across the country from a particular college.
Uniform – Count of students in each department.

Retail:

Longtailed – Revenue from different product categories in a supermarket.
Uniform – Sales of grocery department on 1st of every month in a year.

Sports:

Longtailed – No. of goals Cristiano Ronaldo scored against all the clubs in La Liga.
Uniform – No. of injuries per each team in La Liga in a particlar season.