Applying Machine Learning Techniques to Detect and Understand Fake News
summary
Uses Python, Jupyter and OpenSUSE with pandas, scikit‑learn and NLTK to train a Passive‑Aggressive classifier on 7,200 labeled articles (3,600 fake / 3,600 real) and evaluate on 19,440 held‑out items. Text is tokenized with TF‑IDF and word2vec, visualized with t‑SNE; training runs in ~0.2s. Results: ~91.7–92% accuracy, precision 0.95, recall 0.87, F1 ≈0.91; compares favorably to decision trees, SVM, random forest and logistic regression.
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chapters (17)
- 0:00Introduction
- 0:15What is Machine Learning
- 1:30Who am I
- 2:11What I used
- 3:05Back to the basics
- 4:20Digital Media
- 5:43Machine Learning
- 9:18Data
- 11:40Web Scraping
- 12:12Preprocessing
- 15:59Word to Vac
- 17:22Classification
- 19:00Passive Aggressive
- 21:40Pipeline Summary
- 22:40Demo
- 30:37Results
- 32:20Future Work
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