bikeshare's Introduction

BikeShare

Overview of the analysis:

The purpose of this challenge is to create a bike trip analysis. This tutorial will use Pandas to convert the trip duration column to a DateTime datatype. We’ll then develop a set of visualization tools that will show you the various elements of the trip duration.

The length of time that bikes are checked out for all riders and genders
The number of bike trips for all riders and genders for each hour of each day of the week
The number of bike trips for each type of user and gender for each day of the week.

Results:

This is the link to the tableau public of NYC Bike-Share: link to dashboard

This line graph displays the duration of checkout time; 5 minutes is the peak of the duration for the number of bike-share in the city

This line graph displays the duration of checkout time by gender. Showing males utilize bike-sharing services the most, and each gender follows the same trends maxing out at 5 minutes in the number of bikes.

This graph displays a week of bike-shared usage throughout the week of bike-shared in the city.

This graph displays a week of bike-shared usage throughout the week of bike-shared in the city by gender. Showing males are utilizing the service more.

This graph displays customers vs. subscribers by gender. Male Subscribers make up the majority, followed by female subscribers throughout the week. Thursday has the most usage by males per week.

This graph displays the number of bikes utilized by gender and age. It shows that males with the birth year of 1990 use the bike share service the most.

This graph displays customers vs. subscribers by gender. Male Subscribers make up the majority, followed by female subscribers.

Summary:

The analysis states that many of these users may not be tourists but commuters, as most peak times are around morning and evening commutes. Most of the users were males; this can be useful in marketing campaigns.

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