{"id":15547135,"url":"https://github.com/mgautam98/ford-gobike","last_synced_at":"2026-04-11T20:36:57.507Z","repository":{"id":184766878,"uuid":"260630322","full_name":"mgautam98/ford-gobike","owner":"mgautam98","description":"Ford GoBike Exploration-Analysed and explored a dataset containing trips of 2506983 customer/subscriber from Ford GoBike 2017 data-set","archived":false,"fork":false,"pushed_at":"2020-05-02T06:47:03.000Z","size":1725,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-03T11:14:38.574Z","etag":null,"topics":["data-analytics","data-science","pandas"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mgautam98.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-05-02T06:45:07.000Z","updated_at":"2020-06-26T20:09:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"2972eb7d-9a58-4cb5-9aad-018ec1609519","html_url":"https://github.com/mgautam98/ford-gobike","commit_stats":null,"previous_names":["mgautam98/ford-gobike"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgautam98%2Fford-gobike","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgautam98%2Fford-gobike/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgautam98%2Fford-gobike/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgautam98%2Fford-gobike/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mgautam98","download_url":"https://codeload.github.com/mgautam98/ford-gobike/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246122392,"owners_count":20726823,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analytics","data-science","pandas"],"created_at":"2024-10-02T13:06:55.413Z","updated_at":"2025-10-18T16:30:08.141Z","avatar_url":"https://github.com/mgautam98.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Ford GoBike Dataset\n\n## Dataset\n\nThe 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. \n\n\n## Summary of Findings\n\nIn the exploration, I found the following thing  \n\n- Most of the users are subscribers.\n- Ridership is has increases form June 2017 to October 2017 and then decreases on followng months November, December.\n- 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.\n- 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.\n- starting_station and ending_station are highly related. This means that the users are chosing same set of starting and ending stations.\n- Customer uses the bike for more duration on average than Subscribers.\n- 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.\n- On weekdays ridership increased at 8:00, and 17:00 while on weekends the ridership increases between 11:00 to 15:00\n- At weekends ridership increases at night.\n- The no. of office commuters increased form July, to August drastically.\n\n\n## Key Insights for Presentation\n\nFor the presentation, At the start I introducded with the User type and its\ndistribution.Later, I explained the effect of user decisions on day wise and hour wise\nridership details. Also, how the rides are distributed day wise and hour wise.\n\nI also introducted the ride duration distributon with respect to other categorical variables. Then I high\nlighted between the relation of starting station and ending station. And at last highlighted the traffic at stations\non weekdays vs weekends.  \n\n## Slides\nAccess the slides by running the following command on terminal\n```\njupyter nbconvert slide_deck.ipynb --to slides --post serve --template output_toggle\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmgautam98%2Fford-gobike","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmgautam98%2Fford-gobike","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmgautam98%2Fford-gobike/lists"}