This project provides a comprehensive analysis of cab ride data using Excel, SQL, and Power BI. The analysis identifies patterns, trends, and operational insights to improve decision-making for ride-hailing services.
📌 Project Overview
The Cab Ride Analysis project focuses on key metrics like customer behavior, ride trends, cancellations, and performance of drivers and locations. The project uses SQL for data cleaning and querying, Excel for exploratory analysis, and Power BI for creating dynamic visualizations.
🔍 Features
Data Cleaning and Preparation:
Cleaned raw cab ride data using SQL queries to remove duplicates and handle missing values.
Exploratory Data Analysis:
Performed statistical and trend analysis using Excel.
Aggregated metrics such as total rides, cancellations, average VTAT (Vehicle Time to Arrival), and CTAT (Customer Time to Arrival).
Interactive Dashboards:
Built visual dashboards in Power BI to highlight:
Rides by time, date, and location.
Cancelation reasons (customer/driver-side).
Ride performance by vehicle type (e.g., SUV, Sedan, Bike).
Pickup and drop-off hotspots.
🛠 Tools and Technologies Used
SQL:
Data cleaning, querying, and aggregations.
Extracted insights on canceled rides and performance.
Microsoft Excel:
Exploratory analysis and initial data visualization.
Used pivot tables and charts for summarization.
Power BI:
Designed an interactive and visually appealing dashboard.
Built filters and slicers for drill-down insights.
📈 Key Insights
Ride Patterns:
Peak hours are observed during mornings (7 AM–9 AM) and evenings (5 PM–8 PM).
Cancellation Trends:
Customer Reasons:
Driver not moving towards pickup location.
Change of plans.
Driver Reasons:
Personal issues or vehicle-related problems.
Geographic Insights:
Most bookings are concentrated in high-density urban areas.
Suburban areas experience higher cancellation rates.
Vehicle Performance:
SUVs and Sedans contribute to higher earnings per ride but have longer average VTATs.