https://github.com/sayande01/fake_news_prediction_machine_learning
This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.
https://github.com/sayande01/fake_news_prediction_machine_learning
binary-classification decison-trees gradient-boosting-classifier logistic-regression random-forest
Last synced: about 1 month ago
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This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.
- Host: GitHub
- URL: https://github.com/sayande01/fake_news_prediction_machine_learning
- Owner: sayande01
- Created: 2024-05-14T18:28:49.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-14T18:40:34.000Z (about 1 year ago)
- Last Synced: 2025-02-13T02:39:06.622Z (3 months ago)
- Topics: binary-classification, decison-trees, gradient-boosting-classifier, logistic-regression, random-forest
- Language: Jupyter Notebook
- Homepage:
- Size: 819 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0