An End-to-End Business Intelligence Project
Transforming raw Uber ride booking data into actionable business insights using Python, MySQL, SQL, and Power BI.
The Uber Ride Analytics Dashboard is an end-to-end Business Intelligence (BI) project that demonstrates the complete analytics lifecycle, from raw data preprocessing to interactive dashboard development.
The project follows industry-standard BI practices by building an automated ETL pipeline, designing a Star Schema data warehouse, performing SQL-based business analysis, and developing an executive-friendly Power BI dashboard.
- Clean and preprocess raw Uber booking data.
- Build an ETL pipeline using Python.
- Design a Star Schema data warehouse in MySQL.
- Perform business analysis using SQL.
- Develop an interactive Power BI dashboard.
- Generate business insights and recommendations.
The project follows the complete Business Intelligence workflow.
Raw Dataset
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Data Engineering
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ETL Pipeline
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MySQL Data Warehouse
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SQL Business Analysis
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Power BI Dashboard
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Business Insights
The analytical data warehouse was designed using a Star Schema consisting of:
- 1 Fact Table
- 7 Dimension Tables
This dimensional model enables efficient SQL querying and seamless Power BI integration.
| Category | Technology |
|---|---|
| Programming Language | Python |
| Data Processing | Pandas |
| Database | MySQL |
| ORM | SQLAlchemy |
| Database Connector | PyMySQL |
| Query Language | SQL |
| Business Intelligence | Power BI |
| Notebook Environment | Jupyter Notebook |
| Version Control | Git & GitHub |
| Property | Value |
|---|---|
| Dataset | Uber Booking Dataset |
| Records | 150,000 |
| Columns | 21 |
| Fact Tables | 1 |
| Dimension Tables | 7 |
| Dashboard Pages | 8 |
The dashboard consists of 8 analytical pages, each focused on a specific business domain.
- Executive Summary
- Booking Performance
- Revenue Performance
- Cancellation Analysis
- Vehicle Performance
- Customer Experience
- Location Intelligence
- Time Intelligence
Each page includes:
- Business Question
- Interactive Visualizations
- Business Insights
- Business Recommendations
Uber-Data-Analyst-Project/
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├── dashboard/
│ ├── Uber_Ride_Analytics.pbix
│ └── Uber_Ride_Analytics.pdf
│
├── data/
│ ├── raw/
│ ├── cleaned/
│ └── transformed/
│
├── docs/
│ ├── 01_Project_Overview.md
│ ├── 02_Data_Engineering.md
│ ├── 03_Data_Warehouse_Design.md
│ ├── 04_SQL_Analysis.md
│ ├── 05_PowerBI_Dashboard.md
│ └── 06_Project_Architecture.md
│
├── images/
│ ├── dashboard/
│ ├── project_architecture.png
│ └── star_schema.png
│
├── notebooks/
│ ├── 01_Data_Understanding_And_Cleaning.ipynb
│ ├── 02_Data_Transformation.ipynb
│ └── 03_Load_To_MySQL.ipynb
│
├── sql/
│ ├── 01_Create_Schema.sql
│ └── 02_Business_Analysis.sql
│
└── README.md
Detailed documentation for each project stage is available in the docs folder.
| Document | Description |
|---|---|
| Project Overview | Project objectives, workflow, and technologies |
| Data Engineering | Data understanding, cleaning, and ETL pipeline |
| Data Warehouse Design | Star Schema and dimensional modeling |
| SQL Analysis | Business analysis using SQL |
| Power BI Dashboard | Dashboard design and business insights |
| Project Architecture | End-to-end implementation and learnings |
- Completed rides account for the majority of bookings.
- Auto is the highest-performing vehicle category by bookings and revenue.
- Driver cancellations exceed customer cancellations.
- UPI is the most commonly used payment method.
- Morning experiences the highest booking demand.
- Weekdays generate significantly more bookings and revenue than weekends.
- High-demand pickup locations can be used to optimize driver allocation.
This project demonstrates practical experience in:
- Data Cleaning
- Exploratory Data Analysis (EDA)
- ETL Pipeline Development
- Feature Engineering
- Data Warehousing
- Star Schema Design
- MySQL
- SQL Business Analysis
- Power BI
- DAX Measures
- Data Visualization
- Business Storytelling
- Business Intelligence
Possible future improvements include:
- Automated ETL using Apache Airflow
- Incremental data loading
- Cloud deployment using AWS or Azure
- Power BI Service publishing
- Row-Level Security (RLS)
- Real-time dashboard updates
Derin Girish D G
Engineering Student | Aspiring Data Analyst | Business Intelligence Enthusiast
If you found this project interesting, feel free to ⭐ the repository and connect with me on LinkedIn.
This project is licensed under the MIT License. See the LICENSE file for details.