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mental health!
Analysis on Uber time data in San Francisco and future predictions on it.
best investment property
datahack 2020 business track (Yuxuan Fan, Kailing Ding, Jeffery Wang)
We analyzed youth smoking rates across the country, visualized the survey questions, and classified the questions by topic using deep learning.
hello
Can bachelors/bachelorettes find love?
A Case Study on Diabetes Prevalence Among African-Americans
East vs West: mortality rate
Using the power of data vis/analytics to make SF transportation better. report in zip file
Business trap,
Uber Ride Times
Showing how to phack in the worst possible way.
compare alcohol and taxes
UBER PROJECT
Business Track: Improvements in time to connect Rideshare Partners to Customers and scale up service in Peak Ridership Hours
A Proposal Connecting Uber & Users with Insights to Innovate for Better Efficiency
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Determining correlation between diseases with a deeper analysis into Alcoholism.
Created an ARIMA predictor model to improve the UBER business model.
Exploring the relationship between population and housing prices
Uber data analysis and forecast
elevatr
A web visualization and thorough analysis doing the Business Track for this year’s Datahacks competition, using data given to us to gather information about traffic trends in San Francisco.
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