Data Science Stuff

## about

Collected coursework from Data Science Fundamentals: smaller studies on penguins, PCA, and gradient descent, plus a capstone on academic performance.

Data Science Stuff is the collected work from my Data Science Fundamentals course. It includes smaller studies on penguins, life expectancy, breast-cancer data, PCA, k-nearest neighbors, and gradient descent, plus a larger capstone on academic performance.

The capstone used a UCI dataset with roughly 145 students and 32 demographic, socioeconomic, and academic features. The sample is small, so I treated the project as an exercise in careful modeling and interpretation rather than a basis for claims about students in general.

## capstone

I tested DBSCAN and k-means for student grouping. DBSCAN collapsed to one cluster, and the best k-means result used two clusters with a weak silhouette score near 0.11. That was a useful result in itself: a clean-looking PCA plot did not mean the clusters were especially strong.

For grade prediction, I reduced the sparse eight-class target to low, middle, and high groups. A tuned random forest reached about 64% test accuracy. Permutation importance and SHAP pointed toward prior academic performance, study time, age, reading habits, and family context, but I kept the write-up correlational and avoided turning a small observational dataset into a causal story.

## other work

The rest of the collection covers k-nearest-neighbor classification on Palmer Penguins, country clustering and PCA on WHO life-expectancy data, PCA and classification on the Wisconsin breast-cancer dataset, and a manual gradient-descent implementation with interactive Plotly traces. A separate ethics paper looks at privacy, governance, and the risk of repeating weak data claims until they sound true.

## screenshots

no images — this one lives in the code.