It all began with a casual question I used to ask during Uber rides: “Bhaiya, kitna kamate ho Uber, Ola, Zomato, Swiggy kar ke?” They always had interesting stories; you just had to listen to them.

This is how I transformed my Uber ride data into a Power BI dashboard.

Problem

I’ve been using Uber for the last 3 years and wanted to understand my spending patterns, average trip duration, ride counts including canceled rides, the share percentage of Uber rides, the most traveled month, and distance covered, etc. This curiosity prompted me to go deeper into my Uber ride data.

Task and Action

Request personal data from @Uber covering 1 year to 3 years.
Combine different sheets, such as ride data, and create a new one to use image links in the slicer based on ride category.
Perform data transformation and cleaning to handle nulls/blanks and remove unnecessary columns.
Load data into Power BI, perform data modeling and apply additional transformations as required.
Use DAX measures and custom columns to calculate average trip duration and distance in kilometers.
Identify design concepts aligned with Uber’s brand colors and design the initial structure.
Build the dashboard and publish it to Power BI Services.

Result and KPIs

Over three years, the total amount spent on Uber rides amounted to ₹10.43k.
The cumulative distance traveled across all ride categories over three years is 923.79 km.
Out of a total of 192 rides, 125 were completed, while 62 were canceled, with the remainder categorized as either unfulfilled or canceled by the driver.
In 2022, 40 rides were taken, covering a distance of 283.31 km.
In 2023, 77 rides were taken, covering a distance of 576.56 km.
Up until March 2024, 8 rides were taken, covering a distance of 63.92 km.
The average trip duration is 13 minutes, varying across different ride categories.
Expenditure on Uber Auto rides amounted to ₹1.63k for 10 completed rides, covering a distance of 111.62 km.
Expenditure on Uber Moto rides amounted to ₹3.97k for 82 completed rides, covering a distance of 518.32 km.
Expenditure on Uber Go rides amounted to ₹4.31k for 31 completed rides, covering a distance of 268.71 km.
Expenditure on other ride categories totaled ₹472.00 for 2 completed rides, covering a distance of 25.1 km.
The highest expenditure occurred in May 2022, July 2023, and January 2024.

Key Identifiers

Seasonal Spending Patterns: The occurrence of highest spending in May, July, and January across different years may suggest seasonal trends or specific events driving increased ride usage during those months.
Usage Patterns Over Time: The increasing number of rides and distances covered from 2022 to 2023 indicates a growing reliance on Uber services over the years. This trend could be attributed to factors such as changes in commuting habits, increased urbanization, or improvements in service availability.
Preference for Ride Categories: The distribution of expenditure across different ride categories highlights user preferences and usage patterns. For example, the higher expenditure on Uber Moto rides compared to other categories suggests a significant demand for this mode of transportation, possibly due to factors like convenience, cost-effectiveness, or traffic conditions.
Average Trip Duration: The consistent average trip duration of 13 minutes across various ride categories indicates stability in ride lengths. Understanding the factors influencing trip durations, such as distance traveled, traffic conditions, or destination types, can aid in optimizing service efficiency and customer satisfaction.

Interesting Findings

Uber raised its prices on Sunday, August 21, 2022, as indicated by the upward trend in fare amounts for that date.
There was an increase in fare amounts for the ‘Uber Moto’ category on Monday, March 20, 2023.
Similarly, fares went up again on Tuesday, May 23, 2023, in the same year.

Correlation Between Fare Amount and Date and Fare Amount Changes

On specific days mentioned, Uber increased its fare prices for the ‘Uber Moto’ category. This trend is indicated by the upward trend in fare amounts observed for these particular dates. Various factors, including demand-supply dynamics, peak hours, special events, or promotional campaigns, may influence such price adjustments.

Analyzing fare trends on specific days can help identify patterns and factors driving pricing fluctuations, enabling Uber to optimize pricing strategies and better meet customer demand while maximizing revenue.

Uber Ride Power Bi Dashboard

Insightful analytics into my Uber usage over the past 3 years with this Power BI dashboard. Gain valuable insights into spending habits, trip duration, ride counts, and more. Delve into the data and explore trends to make informed decisions. Explore now!

Conclusion

This project has given me valuable insights into my Uber usage over the past 3 years.

If you’re thinking of taking on an independent project without any guidance, this could be your sure shot. You can request your data from Uber and enjoy the ride.

Working on this project helped me develop an analytical thought process. It’s much better than guided projects, and you’ll feel confident too. This project not only provided insights into my spending habits but also helped in data analysis and interpretation.

If cars interest you, explore the Car Sales Dashboard here for more insights.