Exploring Bike Rental Patterns and Trends in a Metropolitan City using R

Bike rental systems have gained popularity in recent years as an eco-friendly and affordable mode of transportation in urban areas. This study is to analyse bike rental data from a metropolitan city using R programming language to explore rental patterns and trends. The dataset used is a bike rental company that covers two years of bike rentals. Data cleaning and pre-processing is done, followed by exploratory data analysis to identify rental patterns, usage trends, and peak usage hours. To investigate the correlation between bike rental usage and weather conditions statistical analysis has been done. The results showed that bike rentals are more frequent on weekdays than weekends, with peak hours during morning and evening commute times. Furthermore, a negative correlation found between bike rentals and rainfall, indicating that people are less likely to rent bikes during rainy days. Our findings provide insights into bike rental usage patterns and can inform policy decisions to improve bike rental services.