repo to showcase tableau and python data mining skills
created two data files - 2016 and 2017 data. Analysis completed on 2017. note that most of these questions could have been answered in python...although the graphics would not have looked as good
Some of the questions answered:
How many trips have been recorded total during the chosen period? By what percentage has total ridership grown? How has the proportion of short-term customers and annual subscribers changed? Today, what are the peak hours in which bikes are used during summer months? Today, what are the peak hours in which bikes are used during winter months? Today, what are the top 10 stations in the city for starting a journey? (Based on data, why do you hypothesize these are the top locations?) Today, what are the top 10 stations in the city for ending a journey? (Based on data, why?) Today, what are the bottom 10 stations in the city for starting a journey? (Based on data, why?) Today, what are the bottom 10 stations in the city for ending a journey (Based on data, why?) Today, what is the gender breakdown of active participants (Male v. Female)? How effective has gender outreach been in increasing female ridership over the course of the past three years? How does the average trip duration change by age? What is the average distance in miles that a bike is ridden? Which Bikes (by ID) are most likely due for repair or inspection this year? How variable is the utilization by bike ID?