A relational database management system designed using normalization (3NF) to analyze restaurant visits, billing transactions, and revenue patterns for business decision-making.
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This project focuses on designing and implementing a relational database management system for a restaurant business to analyze customer visits, sales transactions, and revenue patterns.
The database is built using normalization principles (up to 3NF) to ensure data integrity, reduce redundancy, and support efficient querying for business analysis.
Restaurants generate large volumes of transactional data related to:
- Customer visits
- Orders and billing
- Payments and revenue
Without a structured database, it becomes difficult to analyze performance,
track customer behavior, and generate meaningful insights.
This project addresses the problem by converting raw transactional data into
a well-structured relational database.
- Design a normalized relational database schema
- Implement database tables with proper primary and foreign keys
- Load and manage restaurant visit and billing data
- Perform analytical queries to extract business insights
- Support reporting on revenue and customer trends
The database is structured using Third Normal Form (3NF) to eliminate data redundancy and maintain consistency.
- Customers
- Restaurants
- Servers
- Visits
- Bills
- Meal Types
- Payment Methods
- Database: MySQL
- Programming Language: R
- Tools:
- RStudio
- SQL
- R Markdown
- Version Control: Git & GitHub
restaurant-database-management-system/ │ ├── data/ # Raw dataset ├── docs/ # Documentation and ERD ├── scripts/ # R scripts for DB creation and loading ├── notebooks/ # Analysis and reports ├── outputs/ # Generated reports ├── README.md └── .gitignore
- Designed relational schema based on functional dependencies
- Created database tables with constraints
- Loaded data using R scripts (ETL process)
- Validated data integrity and relationships
- Executed SQL queries for analysis and reporting
- Identified high-revenue restaurants
- Analyzed visit frequency and customer patterns
- Studied revenue trends across time
- Evaluated customer loyalty behavior
These insights help restaurant management in decision-making related to operations and business growth.
- Practical understanding of DBMS concepts
- Hands-on experience with database normalization
- Writing complex SQL queries
- Integrating R with MySQL
- Applying database systems to real-world business problems
- Add advanced SQL views and stored procedures
- Integrate dashboarding tools (Power BI / Tableau)
- Extend analysis with predictive modeling
- Develop a web-based interface for database interaction
Raushan Yadav
Data Analyst | Database & Analytics