Cyclistic Bike Share Data Analysis

This repository contains the code and documentation for a data analysis project conducted on Cyclistic's bike share data. The project is organized into several phases, including data cleaning, data analysis, data visualization, and recommendations. This README provides an overview of each phase and key findings.

Table of Contents

• Project Overview
• Phase 1: Data Cleaning and Preparation
• Phase 2: Data Analysis
• Phase 3: Data Visualization
• Phase 4: Recommendations
• Conclusion

Project Overview

Cyclistic, a bike-sharing company, has provided data on bike rides taken by its customers. The goal of this project is to analyze the data, gain insights into customer behavior, and provide actionable recommendations to improve Cyclistic's bike-sharing service.

Phase 1: Data Cleaning and Preparation

In this phase, the data was cleaned and prepared for analysis. Key steps included:

• Calculating ride durations based on start and end times.
• Filtering out excessively long or short rides.
• Ensuring data accuracy and reliability.

Phase 2: Data Analysis

During this phase, the data was analyzed to gain a better understanding of customer behavior. Analysis included:

• Summary statistics for ride length.
• Breakdown of customer types (annual members vs. casual riders).
• Total rides and ride durations by customer type.
• Ride length patterns by day of the week and month.
• Differences in ride behavior between customer types.

Phase 3: Data Visualization

Visualizations were created to present key insights:

• Total rides per day of the week for each customer type.
• Usage patterns by time of day.
• Monthly trends in bike usage.
• Average ride lengths by week days and months.
• Bike type preferences among customers.

Phase 4: Recommendations

Based on the analysis, the following recommendations are made:

• Increase marketing efforts targeting leisure riders on weekends and in the summer.
• Encourage annual members to use bikes more regularly throughout the week.
• Evaluate the popularity of docked bikes and consider improvements.
• Focus on improving classic bike fleet.
• Consider offering longer rental periods for casual riders.
• Increase marketing efforts during winter months.

Conclusion

This data analysis project provides valuable insights into customer behavior and preferences for Cyclistic's bike-sharing service. The recommendations aim to optimize the service and enhance the customer experience.

For detailed code, data, and visualizations, please refer to the project files in this repository.

For any questions or inquiries, please contact [Your Name] at [Your Email].