Skip to content

EZBanks/Bike-Share-Data-Analytics-Project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 

Repository files navigation

Bike-Share-Data-Analytics-Project

To view a complete HTML version of the document please click here

The company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

Stakeholders

Lily Moreno: The director of marketing and is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy.

Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Executive Summary

The aim of this analysis is to focus on Cyclistic historical bike trip data to identify trends. More specifically, the analysis should help answer the following question: How do annual members and casual riders use Cyclistic bikes differently? The insights discovered will then help design a new marketing strategy to convert casual riders into annual members as the company’s finance analysts have concluded that annual members are much more profitable than casual riders. This report covers different phases in my analysis to help answer the business questions raised by the management. These phases include Ask questions, Prepare data, Process data, Analyze data, Share data, and Act.

Methodology

Before performing the analysis, the data was collected through a public domain (http://www.divvy-tripdata.s3.amazonaws.com/index.html), then wrangled to make sure it’s cleaned, reliable and error-free by finding and filling missing values and normalizing data. After that, I explored and gained insights through variables, proceeded to data visualization to better capture trends and insights and finally made highly recommendations to the executive team.