In this project I analyzed the Ford Gobike dataset to understand how indivduals use the service. The dataset contains information about users based in greater San Francisco Bay area for the month of February 2019. The data had few tidness issues which were fixed and then visualizations was performed.
In the univariate exploration I observed that the variable duration_sec after log transformation showed a right-skewed distribution. On the other hand the log of distance variable showed a bimodal distribution. I also noticed that most of the users are male subscribers between the age 20-40. Additionally, more people use the service on weekdays than on weekends, between 7-9 and 16-18 being the most popular time.
From the bivariate analysis I oberserved that on average the customers take longer trips than the subscribers. Also, the average trip duration is similar across different genders. Even though less people use the service on weekends, the average trip duration was longer.
Finally, multivariate Exploration revealed that throughout the week the customers consistently take longer trips than the subscribers. Additionally, customers take longer trips on weekends than on weekdays. Furthermore the subscribers take on average same duration of trips throughout the week.
More subscribers use the service when compared to the customers. However, customers on average take longer trips. Morning and evenings are the most popular time when the service is used. Even though more people use the service on weekdays, average trip durations are longer on on weekends.