This Power BI dashboard offers a comprehensive analysis of Uber trip data, providing actionable insights into trip trends, revenue patterns, and operational efficiency.
👉 Click here to view the live Power BI dashboard.
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- The dashboard is designed to analyze key metrics across several dimensions:
- Total Bookings: 103.7K
- Total Booking Value: $1.6M
- Total Trip Distance: 349K miles
- Avg Trip Time: 16 minutes
- It is broken down into three main sections:
- Overview Analysis
- This section provides a high-level summary of the entire trip dataset, including:
- Booking values by Payment Type (e.g., Uber Pay, Cash, Amazon Pay).
- Booking values by Trip Type (Day Trip vs. Night Trip).
- Vehicle Type Analysis (e.g., UberX, Uber Black, Uber Comfort) showing total bookings, value, and distance.
- Location Analysis, identifying the most frequent pickup/dropoff points and the farthest trip.
- Time Analysis
- This section focuses on time-based trends to identify peak demand hours and days:
- Total bookings by Pickup Time (hourly distribution).
- Total bookings by Day Name (weekly trend).
- A heatmap showing total bookings by the hour of the day and day of the week, clearly identifying peak demand windows.
- Details
- A detailed table view of individual trips, showing all the granular data used for the analysis, including Trip ID, Vehicle Type, Booking Value, Pickup Location, and Trip Distance.
- Power BI for visualization
- DAX for custom metrics
- Power Query for data transformation
- Excel/CSV dataset as input source
social-media-analytics-dashboard/
│
├── Social_Media_Analytics.pbix
├── Social Media Analysis (Dataset).xlsx
├── img
└── README.md
- Download the file Uber Dashboard.pbix
- Open it in Power BI Desktop (free tool from Microsoft)
- Explore visuals, filters, and slicers interactively
- Connect your own data if desired
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Identify peak booking hours (e.g., 5 PM - 8 PM) and days (e.g., Saturday and Sunday).
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Determine the most profitable vehicle type (e.g., UberX with $5.83M in booking value).
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Pinpoint high-demand locations for targeted driver deployment and operational planning (e.g., Penn Station/Madison Sq West).
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Analyze payment type preferences to optimize payment processing (e.g., Uber Pay at 67.03%).
Namrata Shitole
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