Abstract
The Unravelling Transportation Trends with Data Engineering and Analytics project delves into the correlation between driver behavior and customer ratings within ride-hailing platforms. Utilizing a dataset akin to Uber's, the study investigates the impact of acceptance rates and cancellation rates on customer satisfaction, aiming to glean valuable business insights for enhancing service quality and fostering business growth. Meticulous data collection and preprocessing ensure data accuracy and reliability, while secure cloud-based storage facilitates efficient data processing. By analyzing driver behavior metrics and customer ratings, the project uncovers pivotal patterns and trends, offering essential guidance for optimizing driver performance. This data-driven approach empowers ride-hailing services to implement targeted strategies for improving service quality, enhancing customer retention, and cultivating a loyal customer base. Keywords—Ride-hailing platforms, Driver behavior, Customer ratings, Data engineering Analytics, Service quality, Customer satisfaction, Data accuracy, Driver performance, optimization
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