https://github.com/elcaiseri/udacity-advanced-data-analysis

UDACITY - Advanced-Data-Analysis Track Project
https://github.com/elcaiseri/udacity-advanced-data-analysis

data-analysis python

Last synced: 15 days ago
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UDACITY - Advanced-Data-Analysis Track Project

• Host: GitHub
• URL: https://github.com/elcaiseri/udacity-advanced-data-analysis
• Owner: elcaiseri
• Created: 2022-03-23T20:28:22.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-24T19:16:05.000Z (almost 3 years ago)
• Last Synced: 2024-11-08T13:12:07.399Z (2 months ago)
• Topics: data-analysis, python
• Language: Jupyter Notebook
• Homepage:
• Size: 1.12 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
# UDACITY - Advanced-Data-Analysis Track Project

## FordGo Bike - Trip Dataset

This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area in **Feb2019.**

**The dataset after cleaning contains 174952 trips with 15 features. The features are:**

1. duration_sec : duration for the trip in second

2. start_station_name : the trip start station name

3. end_station_name : the trip end station name

4. start_station_latitude : start station latitude location

5. end_station_latitude : end station latitude location

6. user_type : Members divided to Subscriber (subscribe to the service) or Customer (normal customer)

7. start_date : the date at which the trip start

8. end_date : the date at which the trip end

9. start_station_longitude : start station longitude location

10. end_station_longitude : end station longitude location

11. start_week : the day of the week at which the trip start (Saterday, Sunday, Monday, Tuesday, Wednesday, Thursday and Friday)

12. end_week : the day of the week at which the trip end (Saterday, Sunday, Monday, Tuesday, Wednesday, Thursday and Friday)

13. start_day : strat day of month (1-31)

14. end_day : end day of month (1-31)

15. bike_share_for_all_trip : bike share for all trip

16. member_birth_year: birth year for user

17. member_gender: user gender (Male, Female)

## Summary of Findings

* High duration trips does not related to gender but and most trips consist of mid age users.

* Age range of subscribers user type are slightly larger than customers.

* Subscriber users uses the 3 main staions locations more than other users types.

* Male spread on the 3 main locations (clusters) more than Females.

* User who started thire journey from the left cluster are more likely to share bike for all trip than users who use bikes from both right locations.

## Key Insights for Presentation

* Distribution for trips over (Duration / Sec , Age, User Type, Member Gender, Bike Share, Start and End Stations).

* Station Locations (latitude and longitude)

* Days of Month and Day of Week

* The correlation between the numerical features.

* The relation between the main features which are (Duration, Age and Gender).