Divvy Trip Data Analysis - Python

Objective: To gain insights into customer behavior and usage patterns of Divvy bikes, a popular bike-sharing service in Chicago.

Data: The analysis utilized historical Divvy bike trip data, including information on start and end stations, timestamps, user types (casual or member), and bike types.

Key Questions:

Customer Preferences: What types of bikes are most popular among Divvy riders? Are there any differences in preferences between casual and member users?

Usage Patterns: When are peak usage times and days of the week? How do these patterns vary seasonally?

Station Popularity: Which stations are the most popular for both starting and ending trips? Are there any geographic trends?

Trip Duration: What is the average trip duration? Are there any significant differences between casual and member users?

Analysis Techniques:

Descriptive Statistics: Calculated summary statistics for variables such as trip duration, start and end times, and bike types.

Data Visualization: Created visualizations like histograms, bar charts, and maps to explore data trends and patterns.

Time Series Analysis: Analyzed seasonal and weekly trends in bike usage to identify peak periods.

Geographic Analysis: Used mapping techniques to visualize station popularity and identify geographic clusters.