Process Automation
UBER RIDE ANALYTICS PROJECT
Coursework Project for DAAN 545
Year :
2025
Industry :
Academic Coursework
Client :
Penn State University
Project Duration :
15 weeks
Problem :
Uber ride data in India showed patterns related to customer behavior, cancellations, pricing, wait times, ride distance, and service reliability, but those patterns needed to be analyzed in a way that could support better business decisions. The project focused on how Uber could reduce cancellations, improve successful bookings, identify high-value riders, and better align pricing and driver availability.
Solution :
Our team analyzed around 150,000 Uber rides using clustering, dimensionality reduction, outlier detection, and frequent pattern mining. These methods helped identify customer segments, cancellation patterns, operational inefficiencies, and possible business strategies for retention, pricing, and service improvements.
Challenge :
The challenge was not just finding patterns in the data, but understanding what those patterns actually meant for real business decisions. Some insights, such as reducing cash cancellations or retaining premium riders, had to be balanced with risks like customer access, regional differences, and operational cost.
Summary :
This project strengthened my experience with data analytics, customer segmentation, business intelligence, and decision-focused reporting. It shows my ability to work with large datasets, identify useful insights, and explain how analytics can support real-world business improvements.
More Projects
Process Automation
UBER RIDE ANALYTICS PROJECT
Coursework Project for DAAN 545
Year :
2025
Industry :
Academic Coursework
Client :
Penn State University
Project Duration :
15 weeks
Problem :
Uber ride data in India showed patterns related to customer behavior, cancellations, pricing, wait times, ride distance, and service reliability, but those patterns needed to be analyzed in a way that could support better business decisions. The project focused on how Uber could reduce cancellations, improve successful bookings, identify high-value riders, and better align pricing and driver availability.
Solution :
Our team analyzed around 150,000 Uber rides using clustering, dimensionality reduction, outlier detection, and frequent pattern mining. These methods helped identify customer segments, cancellation patterns, operational inefficiencies, and possible business strategies for retention, pricing, and service improvements.
Challenge :
The challenge was not just finding patterns in the data, but understanding what those patterns actually meant for real business decisions. Some insights, such as reducing cash cancellations or retaining premium riders, had to be balanced with risks like customer access, regional differences, and operational cost.
Summary :
This project strengthened my experience with data analytics, customer segmentation, business intelligence, and decision-focused reporting. It shows my ability to work with large datasets, identify useful insights, and explain how analytics can support real-world business improvements.
Process Automation
UBER RIDE ANALYTICS PROJECT
Coursework Project for DAAN 545
Year :
2025
Industry :
Academic Coursework
Client :
Penn State University
Project Duration :
15 weeks
Problem :
Uber ride data in India showed patterns related to customer behavior, cancellations, pricing, wait times, ride distance, and service reliability, but those patterns needed to be analyzed in a way that could support better business decisions. The project focused on how Uber could reduce cancellations, improve successful bookings, identify high-value riders, and better align pricing and driver availability.
Solution :
Our team analyzed around 150,000 Uber rides using clustering, dimensionality reduction, outlier detection, and frequent pattern mining. These methods helped identify customer segments, cancellation patterns, operational inefficiencies, and possible business strategies for retention, pricing, and service improvements.
Challenge :
The challenge was not just finding patterns in the data, but understanding what those patterns actually meant for real business decisions. Some insights, such as reducing cash cancellations or retaining premium riders, had to be balanced with risks like customer access, regional differences, and operational cost.
Summary :
This project strengthened my experience with data analytics, customer segmentation, business intelligence, and decision-focused reporting. It shows my ability to work with large datasets, identify useful insights, and explain how analytics can support real-world business improvements.
More Projects
Process Automation
UBER RIDE ANALYTICS PROJECT
Coursework Project for DAAN 545
Year :
2025
Industry :
Academic Coursework
Client :
Penn State University
Project Duration :
15 weeks
Problem :
Uber ride data in India showed patterns related to customer behavior, cancellations, pricing, wait times, ride distance, and service reliability, but those patterns needed to be analyzed in a way that could support better business decisions. The project focused on how Uber could reduce cancellations, improve successful bookings, identify high-value riders, and better align pricing and driver availability.
Solution :
Our team analyzed around 150,000 Uber rides using clustering, dimensionality reduction, outlier detection, and frequent pattern mining. These methods helped identify customer segments, cancellation patterns, operational inefficiencies, and possible business strategies for retention, pricing, and service improvements.
Challenge :
The challenge was not just finding patterns in the data, but understanding what those patterns actually meant for real business decisions. Some insights, such as reducing cash cancellations or retaining premium riders, had to be balanced with risks like customer access, regional differences, and operational cost.
Summary :
This project strengthened my experience with data analytics, customer segmentation, business intelligence, and decision-focused reporting. It shows my ability to work with large datasets, identify useful insights, and explain how analytics can support real-world business improvements.
Process Automation
UBER RIDE ANALYTICS PROJECT
Coursework Project for DAAN 545
Year :
2025
Industry :
Academic Coursework
Client :
Penn State University
Project Duration :
15 weeks
Problem :
Uber ride data in India showed patterns related to customer behavior, cancellations, pricing, wait times, ride distance, and service reliability, but those patterns needed to be analyzed in a way that could support better business decisions. The project focused on how Uber could reduce cancellations, improve successful bookings, identify high-value riders, and better align pricing and driver availability.
Solution :
Our team analyzed around 150,000 Uber rides using clustering, dimensionality reduction, outlier detection, and frequent pattern mining. These methods helped identify customer segments, cancellation patterns, operational inefficiencies, and possible business strategies for retention, pricing, and service improvements.
Challenge :
The challenge was not just finding patterns in the data, but understanding what those patterns actually meant for real business decisions. Some insights, such as reducing cash cancellations or retaining premium riders, had to be balanced with risks like customer access, regional differences, and operational cost.
Summary :
This project strengthened my experience with data analytics, customer segmentation, business intelligence, and decision-focused reporting. It shows my ability to work with large datasets, identify useful insights, and explain how analytics can support real-world business improvements.
More Projects
Process Automation
UBER RIDE ANALYTICS PROJECT
Coursework Project for DAAN 545
Year :
2025
Industry :
Academic Coursework
Client :
Penn State University
Project Duration :
15 weeks
Problem :
Uber ride data in India showed patterns related to customer behavior, cancellations, pricing, wait times, ride distance, and service reliability, but those patterns needed to be analyzed in a way that could support better business decisions. The project focused on how Uber could reduce cancellations, improve successful bookings, identify high-value riders, and better align pricing and driver availability.
Solution :
Our team analyzed around 150,000 Uber rides using clustering, dimensionality reduction, outlier detection, and frequent pattern mining. These methods helped identify customer segments, cancellation patterns, operational inefficiencies, and possible business strategies for retention, pricing, and service improvements.
Challenge :
The challenge was not just finding patterns in the data, but understanding what those patterns actually meant for real business decisions. Some insights, such as reducing cash cancellations or retaining premium riders, had to be balanced with risks like customer access, regional differences, and operational cost.
Summary :
This project strengthened my experience with data analytics, customer segmentation, business intelligence, and decision-focused reporting. It shows my ability to work with large datasets, identify useful insights, and explain how analytics can support real-world business improvements.