This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. This dataset included about 158168 record of bike rents across June 2020. It has the timestamp of each rent and the start and end position, besides the x,y coordinates and the type of the rented bike
In the exploratory phase, I started to know the dataset info and transform every non-usual dtype to its normal state. I knowed my main features of interest and they are (Bike type, the timestamp, the start and end position and distance, member casual). Then I started to plot each feature individually then by pairs to beeter find answers to my questions.
For the presentation, I focus on just the influence of the main features. I started by the distribution of the Bike-Type by a barchart with ratios. Then, I compared distances traveled by a histogram by each user state (member or casual). I made also a nice comparison between each user and another by a barchart to see which type conquer the another. Then, I compared all the days with each other and I concluded that the most user-attracting days are weekends. I found also that users prefer rent bikes afternoon and evening. Finally, I realized that the elecrtic_bike spend less time than the docked_bike but in contrast travel for a longer distance which was a great visualization.