This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. It has 183412 observations (rows) with 16 features (columns). The dataset has datatypes int64(2), float64(7) and object (7). After cleaning techniques were carried on the dataset, there were 173768 observations (rows) and 13 features (columns) with datatypes category(3), datetime64(2), int64(3) and object(5).
Univariate Exploration: From this exploration, we had six(6) distributions plotted. The trip duration distribution (two (2) plots), user age distribution based on the member_age and member_birth_year columns (two distribution plots), one plot for the user_type distribution and one plot for member_gender distribution. The main finding from this exploration is that the users of ages twenty to fifty bike the most and males bike a lot more than females and other.
Bivariate exploration: There were five total plots with four being box plots and the last one being a scatter plot. What was found out in this exploration is that as the age increases, the trip duration increases and customers bike more than subscribers from their comparison with trip duration.
Multivariate exploration: There are seven plots that were used for the exploration and they were all scatter plots. The plots in this exploration helped to strengthen the fact that the age of the users increase then the trip duration decreases and it also helped to cement the fact that users of the male gender bike and have the highest trip duration compared to the other genders of users.
In conclusion, all the plots helped to find that as a user ages (no matter type or gender), the trip duration reduces significantly. The ages where the trip duration is the strongest is between ages twenty (20) to fifty (50).
Trip Duration Distribution. In the presentation, we will look at the log scale histogram of the trip duration distribution and a little analysis into it.
Member_age Distribution. Looking at how the age of the users help to understand how it affects and its relationship to the trip duration.