This section presents the finalized book records of our virtual library. It includes the title, author, genre, publication year, and availability details of 50 books. These entries form the foundation of the digital collection, giving insight into the diversity and accessibility of the catalog.
2. Author Profiles
The authors' dataset includes the birth year and gender of each contributor. This profile helps examine historical representation in literature and allows for gender distribution analysis within the library. It’s a key dataset for inclusion studies and understanding literary demographics.
3. Book Popularity Analysis
Here we evaluate each book’s performance by times borrowed, review ratings, and age group engagement. The popularity status categorizes books as "High", "Medium", or "Low", helping to identify top-performing titles. This analysis supports future recommendation systems and library purchasing strategies.
4. CSV Validation and Relational Mapping
The system screenshot shows our CSV files validated and connected using a visual relational model. Foreign key relations were tested and schema issues addressed. This validation process ensures reliable data structure and prepares the foundation for future data analytics or database development.
Report & Conclusion
The Virtual Library Analysis project simulates a functioning library system using real-world data structure concepts. Each part of the dataset was thoughtfully crafted — from book records and author backgrounds to detailed popularity metrics — to support educational and analytical goals.
By visualizing and validating the relationships between datasets, we ensure a normalized and scalable design. The outcome is a digital library system ready for advanced analytics, recommendation algorithms, or integration into larger educational platforms.
