https://github.com/himank-khatri/spamham

NLP models trained using Bag of Words (BoW) and Term Frequency - Inverse Document Frequency (TF-IDF) to classify SMS as Spam or Ham.
https://github.com/himank-khatri/spamham

bag-of-words naive-bayes-algorithm nlp nlp-machine-learning spam-detection tfidf-vectorizer

Last synced: 8 months ago
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NLP models trained using Bag of Words (BoW) and Term Frequency - Inverse Document Frequency (TF-IDF) to classify SMS as Spam or Ham.

• Host: GitHub
• URL: https://github.com/himank-khatri/spamham
• Owner: Himank-Khatri
• License: mit
• Created: 2024-12-26T07:11:07.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-12-26T07:48:36.000Z (10 months ago)
• Last Synced: 2024-12-26T08:21:20.273Z (10 months ago)
• Topics: bag-of-words, naive-bayes-algorithm, nlp, nlp-machine-learning, spam-detection, tfidf-vectorizer
• Language: Jupyter Notebook
• Homepage:
• Size: 335 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# SpamHam

NLP models trained using Bag of Words (BoW), Term Frequency - Inverse Document Frequency (TF-IDF) and Google's word2vec to classify SMS as Spam or Ham. Trained on [SMS Spam Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset), Kaggle.

## Accuracies Achieved 

- Bag of Words: 98.6%

- TF-IDF: 97.6%

- word2vec: 97.4%