Web Intelligence
Course portal: Web Intelligence — recommendation systems, clustering, search engines, ML classifiers (Python, FastAPI, Svelte)
Web Intelligence
Coursework from the Web Intelligence course (2DV515) at Linnaeus University. Four assignments and a final project covering core information retrieval and machine learning concepts, each implemented as a full-stack application with a Python (FastAPI) backend and Svelte frontend.
Assignments
| # | Repository | Topic | Key Concepts |
|---|---|---|---|
| 1 | Recommendation System | Collaborative filtering | User-based and item-based recommendations, Euclidean distance, Pearson correlation, Pandas |
| 2 | Clustering | Blog post clustering | K-means, hierarchical (agglomerative) clustering, dendrogram visualization |
| 3 | Search Engine | Wikipedia search | PageRank, content-based scoring, word frequency, document location |
| 4 | Machine Learning | Classification | Gaussian Naive Bayes (from scratch), cross-validation, precision/recall/F1 |
Project
| Repository | Topic | Key Concepts |
|---|---|---|
| Text Classification | Wikipedia articles | TF-IDF, MultinomialNB, LinearSVC, scikit-learn pipelines, grid search |
Tech Stack
- Backend: Python, FastAPI
- Frontend: Svelte, TypeScript
- ML/Data: Pandas, NumPy, scikit-learn, ChromaDB
- Other: Vite, Docker
