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Data Analysis on 'Captial Bikeshare : A bike sharing service in the United States'

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Trip-History-Analysis

Data Analysis on 'Captial Bikeshare : A bike sharing service in the United States'

For more detailed documentation look into docs/

Instructions to run

(requires python 3+)

  1. Install all libraries using(requirements file) : pip3 install -r requirements.txt
  2. Run the ui.py file

Screen

screen

Few Notes

The data includes:

  • Duration – Duration of trip
  • Start Date – Includes start date and time
  • End Date – Includes end date and time
  • Start Station – Includes starting station name and number
  • End Station – Includes ending station name and number
  • Bike Number – Includes ID number of bike used for the trip
  • Member Type – Indicates whether user was a "registered" member (Annual Member, 30-Day Member or Day Key Member) or a "casual" rider (Single Trip, 24-Hour Pass, 3-Day Pass or 5-Day Pass)

This data has been processed to remove trips that are taken by staff as they service and inspect the system, trips that are taken to/from any of our “test” stations at our warehouses and any trips lasting less than 60 seconds (potentially false starts or users trying to re-dock a bike to ensure it's secure).

NOTE: The 3-Day Membership replaced the 5-Day Membership in Fall 2011

Classification Algo

Classes

  1. Registered (member)
  2. Casual (casual)
  • Logistic Regression
  • Naive Bayes
  • KNN

Preprocessing

  • Selecting duration, station (start and end ids), class (member, casual) (These are the saved models accuracies)
  1. KNN accuracy:

    • Custom : 95.15
    • SKLearn : 91.30
  2. NB accuracy:

    • Custom : 89.90
    • SKLearn : 91.45
  3. LR accuracy:

    • Custom : 88.71
    • SKLearn : 90.17

Directory details

  1. processed_dataset/ : processed csv file
  2. charts : plots to visualize data
  3. lib : custom implementations of all the algos
  4. model : saved pre-trained model

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Data Analysis on 'Captial Bikeshare : A bike sharing service in the United States'

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