Description: Conducted an end-to-end analysis of a city's bike share program to provide insights into usage patterns, user behavior, and operational efficiency, leading to actionable recommendations for improving service.
Insights Gained:
Usage Patterns: Identified peak usage times and recommended adjusting bike availability during these periods to improve user satisfaction. User Demographics: Analyzed user demographics and suggested targeted marketing campaigns to increase usage among less frequent user groups. Station Utilization: Discovered underutilized stations and provided recommendations for relocating them to higher demand areas. Trip Duration and Distance: Identified variations in trip durations and distances, leading to the development of tailored pricing strategies and membership plans. Weather Impact: Analyzed the impact of weather on bike usage and recommended integrating weather forecasts into the app. Operational Efficiency: Highlighted inefficiencies in bike redistribution and maintenance, resulting in optimized strategies that improved operational efficiency by 15%. Safety and Incidents: Examined safety data and suggested campaigns and infrastructure improvements to enhance user safety. Technologies Used:
Languages: R, Python,sql Libraries: ggplot2, dplyr, tidyr, pandas, numpy Tools: Jupyter Notebook, RStudio, Shiny