Exploratory Data Analytics on Capital Bikeshare

Data Information

All data shared from CapitalBikeShare

Project Introduction

In the last decade there has been increasing concern regarding the environment and the quality of life, especially in big cities.

Tasks to make

• Identify the variables that most impact hourly ridership
• Develop a model to predict hourly bikeshare demand in the Greater Washington DC region based on historical ridership and weather data for next year
• Get a total of members and casual clients
• Explore how many clients use in normal work/study hour 6 ~ 9 12 ~ 14 18 ~ 20
• Get the most stations used
• Get the most bikes used by the time used

Exploration Data Analysis and Visualization

I will focus on the exploration analysis of the capital bikeshare data for 2017Q1 initially. This data exploration stage then focused on visualizing these relationships and patterns to make it easier for the audience to understand.