Uber Rides Analysis Project Project Overview

This project focuses on analyzing Uber rides data to extract meaningful insights. Using SQL for querying and Python (with Pandas and Plotly) for analysis & visualization, the project answers key business questions such as:

Which vehicle types generate the most revenue?

Which pickup location generates the highest reveneue?

Reasons for customer ride cancellation?

What are the reasons for driver's ride cancellation?

What is the average driver ratings per vehicle type?

The goal is to demonstrate how data-driven insights can improve decision-making in a ride-sharing business.

Tools & Technologies

SQL (SQLite) → For querying and aggregating ride data

Python → Data analysis and visualization

Pandas → Data handling and preprocessing

Plotly → Interactive visualizations

Jupyter Notebook → Development environment

Key Insights

Revenue Analysis → Identified top-performing vehicle categories.

Customer Behavior → Found the most loyal and high-spending customers.

Peak Hours → Analyzed time trends to find rush hours for rides.

Cancellations → Studied cancellation reasons to improve service.

Ratings → Checked driver and customer satisfaction trends.