NYC-Citibike-Analysis

This analysis was built using Tableau Public.

Overview of the statistical analysis:

The main objective of the project is to analyze and visualize the Citibike data to gain insights into the patterns of bike usage in NYC. The project uses Python for data wrangling and analysis and Tableau for data visualization. The project also includes the use of various Python libraries, such as Pandas, Matplotlib, and Seaborn, to perform data cleaning, manipulation, and visualization.

The project involves exploring various aspects of the Citibike program, including trip duration, station locations, user demographics, and popular routes. Through the data analysis and visualization, the project aims to identify patterns and trends in Citibike usage in different parts of the city, different times of the day, and different days of the week.

Some of the key insights obtained from the analysis include the most popular starting and ending stations, the most common trip durations, and the busiest times of day and days of the week for Citibike usage. The project also provides visualizations of the data, such as heat maps, scatterplots, and bar charts, to help understand the patterns and trends in the data.

Overall, the NYC Citibike Analysis project is a great example of how data analysis and visualization can be used to gain insights into large datasets and help organizations make data-driven decisions. One potential future improvement could be to incorporate machine learning algorithms to build predictive models or perform classification analysis on the Citibike data.

Create a set of visualizations to:

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

Resources:

• Sofware: Tableau Public, Jupyter Notebook
• Languages: Python

Objectives:

Work with data visualization software called Tableau to present a business proposal for a bike-sharing company and convince investors that a bike-sharing program in Des Moines is a solid business proposal.

• Deliverable 1: Change Trip Duration to a Datetime Format
• Deliverable 2: Create Visualizations for the Trip Analysis
• Deliverable 3: Create a Story and Report for the Final Presentation

Results:

Deliverable 1: Change Trip Duration to a Datetime Format in Jupyter Notebook using Pandas and Datetime libraries.

Deliverable 2: Create Visualizations for the Trip Analysis

1. Checkout Times for Users

2. Checkout Times by Gender

3. Trips by Weekday for Each Hour

4. Trips by Gender (Weekday per Hour)

5. User Trips by Gender by Weekday

Deliverable 3: Create a Story and Report for the Final Presentation

NYC Citibike Analysis Story (all descriptions are added to captions)

NYC Citibike Analysis Story includes all the visualizations described above as well as an added dashboard showing a total # of rides, usage breakdown by gender, average trip duration by age, and top starting locations.

Summary:

From the NYC Citibike Analysis we can draw some conclusions that proof the success of the bikeride sharing model:

1. Most bikes get checked out for less than an hour.
2. Majority of bikes get checked out by males (over 100K) followed by female riders (over 30k).
3. The busiest time for bike checkouts fall on Thursday afternoons between 5:00 and 7:00pm.
4. The largest usertype are male Subscribers.

This is a successful business model because it proves a fast turnaround on the service, shows popularity, is highly marketable to male audience, and it generates a loyal customer fanbase.