Bike Share Analysis

This case study is recommended for Google-data-analytics professional course.

Click to watch in Kaggle

In this case study, I used R language in which used tidyverse and dplyr for data cleaning and manipulation and ggplot2 for data visualization and data analysis.

Observations:

1. Rides of casual riders increases in weekend while membership riders use on weekday more often.
2. In June-Sept number of rides increase frequently.
3. Casual riders trip time is 96% greater than member riders regardless of day or month.
4. All riders take more longer trips in weekend, member riders' ride time increases with 19% and casual riders' ride time increase with 22% .
5. Average ride time stays relatively high for casual riders for March-September.
6. All users ride almost same distance regardless of day or month.
7. Number of rides taken by members is 37% more than casual riders.
8. Although classic bike used for more number of rides electric bikes are used for longer distance and docked bikes are used for longer duration of rides.

Recommendations:

1. We can provide weekend pass to attract casual riders and for member riders we can provide them coupon for renew their ridership.
2. We can provide refferal coupon to member riders when someone make subscribe the membership, by this we can get to new customer and attract casual riders to become member riders.
3. There is a high increment in trip in month of Mar-June or/and June-Sept we can make a 4 months subscription plan for casual users.