Project Highlight

This interactive Power BI dashboard analyzes Uber ride data to uncover patterns in cancellations, peak hours, ride efficiency, and distance trends. The dashboard supports operational optimization and better decision-making.

Data Preparation & Modeling

Data Preparation:

Cleaned and transformed data using Power Query and DAX. Derived binary columns simplified mixed-status fields:

• Cancelled Rides by Driver
• Cancelled Rides by Customer
• Reason for Cancelling by Customer
• Driver Cancellation Reason
• Incomplete Rides
• Incomplete Rides Reason

Null handling was achieved using visual-level and page-level filters to avoid cascading row losses, preserving nearly 90% of the dataset.

DAX Calcluations

Segmented key metrics to identify behavioral and operational patterns:

• Time of Day – Categorized rides into Morning, Afternoon, Evening, and Night based on booking timestamps.
• Distance Range – Bucketed rides into 5 km increments up to 50 km for profitability and demand segmentation.

Time of Day = 
VAR Hour = HOUR('Uber Ride Analytics Dataset 2024'[Time])
RETURN
SWITCH(
    TRUE(),
    Hour >= 5  && Hour < 12, "Morning",
    Hour >= 12 && Hour < 17, "Afternoon",
    Hour >= 17 && Hour < 21, "Evening",
    Hour >= 21 || Hour < 5,  "Night"
)

Distance Range = 
SWITCH(
    TRUE(),
    'Uber Ride Analytics Dataset 2024'[Ride Distance] < 10, "1 - 10",
    'Uber Ride Analytics Dataset 2024'[Ride Distance] < 20, "11 - 20",
    'Uber Ride Analytics Dataset 2024'[Ride Distance] < 30, "21 - 30",
    'Uber Ride Analytics Dataset 2024'[Ride Distance] < 40, "31 - 40",
    'Uber Ride Analytics Dataset 2024'[Ride Distance] < 50, "41 - 50",
    "null"
)

This project demonstrates strong capability in Power BI, DAX, Power Query, and analytical storytelling.