Project 2 - Supply and Demand Gap Analysis

AIM OF THE PROJECT

The overarching goal of this project is to meticulously collect and analyze data related to the pickup points categorized as Airport and City Request IDs in the Uber Request dataset. The specific focus is on understanding the dynamic interplay between demand and supply and its impact on customer travel demand.

INTRODUCTION

This project is undertaken with the explicit task of evaluating the utilization of Uber cab services by passengers. The analysis is designed to ascertain the number of passengers who have successfully completed their rides, those whose rides have been cancelled, and those left without rides due to unavailability of cabs. The project's core objective is to conduct a comprehensive analysis of these scenarios to glean insights into the patterns and factors influencing passenger interactions with Uber services.

PROBLEM STATEMENT

Within the scope of this project, our objective is to meticulously examine several key metrics in the Uber service domain. These metrics include the determination of the number of request IDs originating from both Airport and city pickup points, the assessment of the current driver availability, the quantification of completed trips, and the analysis of cancelled trips. Additionally, our focus extends to understanding the instances where cars are unavailable for scheduled trips. This project aims to provide a comprehensive and formal analysis of these critical aspects within the Uber service framework.

METHODOLOGY

1. Data Processing and Cleaning: Data processing and cleaning involved minimal efforts due to pre-processed data; during data import into Power BI, adjustments were made to column data types for better comprehension.

2. Timeline: Data collection was exclusive to the year 2016.

3. Visualization Techniques: Visualization techniques encompassed the use of cards, vertical list, dropdown lists, donut charts, bar charts, and maps.

4. Tools and Software: Utilized Microsoft Excel for data preprocessing and basic analysis, and Microsoft Power BI for advanced data visualization and dashboard creation.

5. Data Collection : For this project, the dataset used was sourced from Kaggle, specifically from the "Uber Supply-Demand Gap" dataset by hellbuoy. The dataset can be accessed in the link provided [https://www.kaggle.com/datasets/hellbuoy/uber-supplydemand-gap]. It provides valuable information on Uber's supply and demand dynamics, enabling a comprehensive analysis of the factors affecting the service.

ANALYSIS AND INSIGHTS

During observation, several noteworthy findings have been identified, outlined as follows:

· During this analysis, it was observed that a cumulative total of 301 drivers facilitated a demand for 6745 trips. Out of these requests, 4095 rides were concluded, either successfully completed or cancelled. Notably, in 2650 instances, passengers were unable to secure rides due to unavailability of cars, constituting approximately 39.31% of the total requests.

· Examining the origin of Uber cab requests, it was found that 49% of requests originated from airport pickups, while the remaining 51% were initiated from city locations.

Upon data visualization, it was revealed that out of the 6.7k rides requested:

• Approximately 41.79% (2.8k rides) were successfully completed.
• Approximately 40.30% (2.7k rides) encountered unavailability of cabs.
• Approximately 19.40% (1.3k rides) were cancelled.

RECOMMENDATION

• Optimize and balance the distribution of available cabs between airport and city pickup points to mitigate instances of unavailability.
• Enhance operational strategies to reduce the cancellation rate, contributing to an improved overall ride experience for passengers.
• Implement continuous monitoring and adjustment mechanisms to ensure a responsive and seamless Uber service.

These recommendations are crucial for maintaining high customer satisfaction and operational efficiency in the Uber service.

CONCLUSION

In summary, this project shed light on key aspects of Uber ride requests, revealing a balanced distribution between airport and city pickups. Data visualization exposed that out of 6.7k ride requests, approximately 41.79% were successfully completed, 40.30% faced unavailability, and 19.40% were cancelled. The insights emphasize the importance of optimizing cab distribution and refining operational strategies to enhance service reliability and customer satisfaction. Moving forward, implementing these recommendations will contribute to a more responsive and efficient Uber service.