ProposedThis Lab offers a wide range of hands-on projects for students in the fields of Electrical Engineering and Computer Science. Designed to bridge theory and practice, the projects challenge students to apply their academic knowledge to real-world engineering problems, encouraging innovation, teamwork, and independent thinking:
This project explores the design and implementation of supervised machine learning models for predictive data analysis, with a focus on real-world applications such as healthcare, finance, and smart systems. Machine learning (ML) has emerged as a key driver of modern data science, enabling systems to learn patterns from data and make intelligent decisions without explicit programming.
The project begins with data preprocessing techniques such as normalization, feature selection, and handling missing values. We then implement and compare various supervised learning algorithms—including linear regression, decision trees, support vector machines, and neural networks—evaluating their performance using standard metrics such as accuracy, precision, recall, and F1-score.
To ensure robustness and generalization, we employ techniques like cross-validation, hyperparameter tuning, and regularization. Tools such as Python, scikit-learn, TensorFlow, and pandas are used for model development, training, and evaluation. A case study is conducted using a real-world dataset (e.g., medical diagnostics, stock prediction, or customer behavior analysis), demonstrating the end-to-end machine learning pipeline.
The goal of this project is to develop accurate, interpretable, and scalable ML models, while gaining a deep understanding of their theoretical foundations and practical deployment. The outcomes support better decision-making processes and lay the groundwork for future research in data-driven intelligence.
