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: about 1 month 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 (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-02T06:47:03.000Z (over 4 years ago)
• Last Synced: 2024-10-09T13:25:10.440Z (about 1 month 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

```