This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. Data Wrangling was performed to correct datatype, missing values, and create columns for investigations.
1- Bike trips are more frequent on weekdays, particularly on Thursday, Tuesday, and Wednesday, indicating a preference for commuting or weekday activities.
2- There is a significant gender disparity in bike usage, with males accounting for approximately three times the number of female users.
3- The majority of bike service customers are subscribed members, highlighting the importance of subscriber loyalty and the benefits of subscription-based models.
4- Users are primarily in the 30-40-year-old age range, with the average age being around 38 years old. This suggests that the service appeals predominantly to individuals in their late 20s to early 40s.
5- Most bike trips have a duration between 3 and 20 minutes, indicating that the service is primarily used for short-distance travel or quick trips within the city.
6- The station located at Market St at 10th St. is the most popular starting point for bike trips, suggesting its convenience or popularity among users.
7- Peak usage hours for the bike service are around 8 AM and 5 PM, aligning with typical morning and evening commuting times.
8- Trip durations show a similar range across genders, indicating that trip duration does not show significant gender-based differences.
9- The average age for both subscriber and customer user types is similar, indicating that age is not a differentiating factor between these user categories.
10- Female users tend to be slightly younger compared to males and other gender categories, suggesting potential differences in preferences or usage patterns.
11- The highest number of users, in terms of both age and gender, falls within the 26-35-year-old male category.
12- Subscribers account for a higher number of trips during weekdays and days of the week, indicating more frequent usage compared to customers.
13- Males show a higher number of trips during weekdays and days of the week, indicating their higher level of engagement with the bike service during these periods.
14- Subscribers are responsible for the majority of users during high-volume hours, busy weekdays, and days of the week, indicating the impact of the subscription model on consistent usage.
15- The average age distribution shows similarities among genders, with males generally being older than females and other gender categories.
16- Strong correlations are observed between age and hour, as well as start_station_id and end_station_id with age. However, these correlations may not be directly relevant to the analysis conducted.
These findings provide insights into user behavior, gender distribution, age demographics, and usage patterns within the bike service, which can be valuable for strategic planning, marketing campaigns, and service improvements.