🚕 Uber Supply–Demand Gap Analysis

📌 Project Overview

This project analyzes the supply–demand gap in Uber ride requests to identify patterns in cancellations, unfulfilled requests, and driver availability issues. The analysis uses Excel, MySQL, and Python-based Exploratory Data Analysis (EDA) to derive actionable business insights.

🎯 Problem Statement

Uber often faces a mismatch between ride requests (demand) and driver availability (supply), resulting in ride cancellations and “No Cars Available” scenarios. This project aims to analyze historical Uber request data to identify when and where these gaps occur and propose data-driven solutions.

🧠 Business Objective

Identify peak demand periods with low driver availability
Analyze cancellation and unfulfilled request patterns
Compare supply–demand behavior across City and Airport pickup points
Provide actionable recommendations to reduce cancellations and improve ride completion rates

🧰 Tools & Technologies Used

Excel – Data cleaning, pivot tables, dashboards
MySQL – Data querying and aggregation
Python – Pandas, Matplotlib, Seaborn
Jupyter Notebook – Exploratory Data Analysis
GitHub – Version control and documentation

📂 Dataset Description

The dataset contains Uber ride request records with the following attributes:

Request ID
Pickup Point (City / Airport)
Driver ID
Trip Status (Completed / Cancelled / No Cars Available)
Request Timestamp
Drop Timestamp

Each row represents a single Uber ride request.

🔍 Project Workflow

Data understanding and cleaning
Excel-based analysis using pivot tables and charts
SQL-based aggregation and validation
Python EDA with univariate, bivariate, and multivariate analysis
Insight generation and business recommendations

📊 Key Insights

Ride demand peaks during early morning and evening hours
Airport pickups experience higher supply shortages than city pickups
High cancellation rates occur during peak demand periods
“No Cars Available” cases are prominent during night and early morning

💡 Business Recommendations

Introduce time-based and location-based driver incentives
Improve airport-specific driver allocation
Use historical demand forecasting to position drivers in advance
Reduce cancellations through better driver engagement strategies

▶️ How to Run the Notebook

Clone the repository
Place Uber_Request_Data.csv in the same directory as the notebook
Open Uber_Supply_Demand_Gap_EDA.ipynb
Run Kernel → Restart & Run All

🏁 Conclusion

This project demonstrates how Excel, SQL, and Python can be combined to analyze real-world business problems. The insights help identify supply–demand gaps and provide data-driven recommendations to improve Uber’s operational efficiency.

👤 Author

Mohammad Imtiaz Ahmed
Exploratory Data Analysis Project

Imtiazsalaf-01/Uber_Data_Analytics_Project