https://github.com/hardchik/fake-news-detection
https://github.com/hardchik/fake-news-detection
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/hardchik/fake-news-detection
- Owner: Hardchik
- Created: 2021-09-09T16:01:16.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-09T16:17:45.000Z (almost 5 years ago)
- Last Synced: 2025-07-04T06:37:59.438Z (12 months ago)
- Language: Jupyter Notebook
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Fake-News-Detection
This project is based on Supervised Learning in Machine Learning using Passive Aggressive Classifier.
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Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector. -
This advanced python project of detecting fake news deals with fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then, we initialize a Passive Aggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.