IBM Data Science Case Study
Your client, The Mayor of New York City, needs a better understanding of Citi Bike ridership. He wants an Operating Report for January 2017 on his desk by the end of the week. Based on previous engagements we know the mayor is a big fan of visualizing data in charts.
Luckily, Citi Bike publishes quarterly trip data available for you to download and analyze. The data includes:
- Trip Duration (seconds)
- Start Time and Date
- Stop Time and Date
- Start Station Name
- End Station Name
- Station ID
- Station Lat/Long
- Bike ID
- User Type
- (Customer = 24-hour pass or 3-day pass user; Subscriber = Annual Member)
- Gender (Zero=unknown; 1=male; 2=female)
- Year of Birth
Data can be downloaded here:
The client wants to see a variety of data visualizations to answer the following questions:
- Top 5 stations with the most starts (showing # of starts)
- Trip duration by user type
- Most popular trips based on start station and stop station)
- Rider performance by Gender and Age based on avg trip distance (station to station), median speed (distance traveled / trip duration)
- What is the busiest bike in NYC in 2017? How many times was it used? How many minutes was it in use?
Additionally, the Mayor has an idea that he wants to pitch to Citi Bike and needs your help proving its feasibility. He would like Citi Bike to add a new feature to their kiosks:
“Enter a destination and we’ll tell you how long the trip will take”.
We need you to build a model that can predict how long a trip will take given a starting point and destination. You will need to get creative about the factors that will predict travel time. Include model evaluation statistics and discussion of predictive features.