Taught by https://www.cs.emory.edu/~kshu5/
This course was about learning the foundations and techniques of data mining — how to find patterns, trends, and insights from large datasets. We worked with real-world data to understand how to clean it, explore it, and build models that make predictions or discover hidden structures.
We learned how algorithms like decision trees, clustering, and association rules work under the hood, and implemented many of them from scratch in Python. We also practiced evaluating models using metrics like accuracy, precision, and recall, and understood the tradeoffs between different methods.
Heart Disease Prediction Project
https://colab.research.google.com/drive/1XIvxhkd4mvGLCi4O0AsGyyo84GbjQGLK?usp=sharing 1 [PPT 2a]