🚗 Uber Ride Analytics — 2024

📌 Project Overview

This project performs an exploratory data analysis (EDA) of the Uber Ride Analytics Dataset 2024, containing approximately 148K bookings.

The goal is to analyze Uber's ride operations from different business perspectives, including demand patterns, cancellations, revenue, routes, incomplete rides, and customer/driver satisfaction.

The analysis was developed using Python, Pandas, NumPy, Matplotlib, Seaborn, and Plotly.

---

📂 Dataset

The dataset used in this project is the Uber Ride Analytics Dataset 2024, available on Kaggle.

🔗 Dataset: Uber Ride Analytics Dashboard

The dataset contains approximately 148K bookings with information about ride demand, vehicle types, locations, cancellations, revenue, payment methods, ride distances, and customer/driver ratings.

🎯 Objectives

The analysis aims to answer questions such as:

• When is Uber demand the highest?
• How does ride demand vary by day and month?
• Which pickup and drop-off locations are most popular?
• Which routes are used most frequently?
• How does cancellation behavior vary by vehicle type and hour?
• What are the most common reasons for customer cancellations?
• Which vehicle types generate the most revenue?
• Which payment methods contribute the most revenue?
• Which routes generate the highest revenue?
• How much potential revenue is lost from incomplete rides?
• Which vehicle types have the highest breakdown rates?
• Are customer and driver ratings affected by vehicle type or ride distance?

---

📊 Analysis Covered

1. Demand Analysis

• Bookings by day of the week
• Bookings by month
• Bookings by hour
• Peak and low-demand periods
• Pickup and drop-off location analysis
• Most frequent routes

2. Cancellation Analysis

• Cancellation rate by vehicle type
• Cancellation rate by hour
• Dates with the highest number of cancellations
• Customer cancellation reasons

3. Revenue Analysis

• Monthly revenue trends
• Revenue by vehicle type
• Average revenue per ride
• Revenue by payment method
• Revenue-generating routes
• Revenue per kilometer
• Relationship between ride distance and booking value

4. Incomplete Ride Analysis

• Reasons for incomplete rides
• Potential revenue lost from incomplete rides
• Revenue lost due to vehicle breakdowns
• Breakdown count by vehicle type
• Breakdown rate by vehicle type

5. Customer & Driver Satisfaction

• Customer rating distribution
• Driver rating distribution
• Ratings by vehicle type
• Customer ratings across different ride-distance groups

---

🔍 Key Findings

🚦 Demand

• 7 PM is the peak booking hour.
• Demand is lowest during the early morning hours, particularly 12 AM–4 AM.
• Demand varies relatively little across days of the week.
• July and January have among the highest monthly demand, while February has the lowest, partly influenced by having only 28 days.

🚫 Cancellations

• Cancellation rates are relatively consistent across vehicle types, remaining around 24–25%.
• Go Sedan has the highest cancellation rate at approximately 25.29%.
• Wrong Address is the most common customer cancellation reason.
• Evening hours have the highest number of cancellations, largely because overall booking volume is higher during these periods.

💰 Revenue

• March generated the highest monthly revenue at approximately 4.6M, while February generated approximately 4.1M.
• Auto generates the highest total revenue at approximately 12.88M.
• Go Sedan has the highest average revenue per ride at approximately 511.50.
• UPI is the largest contributor to revenue, accounting for approximately 44.5% of total revenue.
• The correlation between Ride Distance and Booking Value is approximately 0.005, indicating almost no linear relationship between the two variables.

⚠️ Incomplete Rides

• Incomplete rides are almost evenly distributed between Customer Demand, Vehicle Breakdown, and Other Issues.
• Auto has the highest number of breakdowns because it also has a larger booking volume.
• After accounting for booking volume, Go Sedan has the highest breakdown rate, while Uber XL has the lowest.
• Vehicle breakdowns represent a significant source of potential lost revenue.

⭐ Satisfaction

• Customer ratings are generally high, with most ratings concentrated around 4.2–4.9.
• Go Sedan has the highest average customer rating at approximately 4.41.
• Uber XL has the highest average driver rating at approximately 4.24.
• Customer ratings remain very consistent across ride-distance groups, suggesting that ride distance has little impact on customer satisfaction.

---

🛠️ Technologies & Libraries

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Plotly
• Jupyter Notebook

---

📁 Project Structure

Uber-Ride-Analytics/
│
├── Uber_Ride_Analytics.ipynb
├── README.md
└── .gitignore