https://github.com/mgautam98/ford-gobike
Ford GoBike Exploration-Analysed and explored a dataset containing trips of 2506983 customer/subscriber from Ford GoBike 2017 data-set
https://github.com/mgautam98/ford-gobike
data-analytics data-science pandas
Last synced: 3 months ago
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Ford GoBike Exploration-Analysed and explored a dataset containing trips of 2506983 customer/subscriber from Ford GoBike 2017 data-set
- Host: GitHub
- URL: https://github.com/mgautam98/ford-gobike
- Owner: mgautam98
- Created: 2020-05-02T06:45:07.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-02T06:47:03.000Z (about 6 years ago)
- Last Synced: 2025-02-03T11:14:38.574Z (over 1 year ago)
- Topics: data-analytics, data-science, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 1.65 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# Ford GoBike Dataset
## Dataset
The data consists of information regarding 2,50,6983 ride data from Ford GoBike Bike renting service. The attributes included duration_sec, start_time, end_time, start_station_id, end_station_id, start_station_name, end_station_name, bike_id, user_type, month, day, and hour details.
## Summary of Findings
In the exploration, I found the following thing
- Most of the users are subscribers.
- Ridership is has increases form June 2017 to October 2017 and then decreases on followng months November, December.
- Ridership is almost constant on weekdays (Mon-Fri) then decreses on weekends. Also, ridership is highest on 8:00 and 17:00, suggests that users are office commuters.
- San Francisco Caltrain (Townsend St at 4th St) is station with highest deficit on no. of bikes. That is more no. of bikes given then no. of bikes deposited.
- starting_station and ending_station are highly related. This means that the users are chosing same set of starting and ending stations.
- Customer uses the bike for more duration on average than Subscribers.
- The no. of rides by customer increases as the no. of rides by subscribers decreases. At 14:00 the subscribers ridership is min (local) while the customer ridership is at maximum.
- On weekdays ridership increased at 8:00, and 17:00 while on weekends the ridership increases between 11:00 to 15:00
- At weekends ridership increases at night.
- The no. of office commuters increased form July, to August drastically.
## Key Insights for Presentation
For the presentation, At the start I introducded with the User type and its
distribution.Later, I explained the effect of user decisions on day wise and hour wise
ridership details. Also, how the rides are distributed day wise and hour wise.
I also introducted the ride duration distributon with respect to other categorical variables. Then I high
lighted between the relation of starting station and ending station. And at last highlighted the traffic at stations
on weekdays vs weekends.
## Slides
Access the slides by running the following command on terminal
```
jupyter nbconvert slide_deck.ipynb --to slides --post serve --template output_toggle
```