CompletedOver the years, students in This Lab have completed a wide range of innovative and high-quality projects. Many of these projects have been showcased in academic conferences, industry collaborations, and final-year exhibitions, reflecting the creativity, technical skills, and problem-solving abilities of our students:
2024
This project investigates unsupervised machine learning techniques for discovering hidden patterns and structures in large and complex datasets. Unlike supervised learning, unsupervised learning does not rely on labeled data, making it a powerful tool for exploratory data analysis, anomaly detection, and data segmentation in domains such as marketing, cybersecurity, and scientific research.
The project focuses on core clustering algorithms, including K-means, DBSCAN, and hierarchical clustering, as well as dimensionality reduction methods such as Principal Component Analysis (PCA) and t-SNE. These methods are applied to diverse datasets to identify natural groupings, reduce complexity, and visualize high-dimensional data. The project includes preprocessing stages such as data cleaning, feature scaling, and outlier handling, followed by implementation and evaluation of clustering performance using internal validation metrics (e.g., silhouette score, Davies-Bouldin index). Tools such as Python, scikit-learn, and Seaborn are used for development, analysis, and visualization.
By the end of the project, we aim to extract meaningful insights from raw, unlabeled data, demonstrating the potential of unsupervised learning to reveal structure, support decision-making, and serve as a foundation for further data modeling and segmentation.
