https://github.com/vansh-khaneja/spam-email-detection

This is a spam email detection model
https://github.com/vansh-khaneja/spam-email-detection

machine-learning naive-bayes-classifier scikit-learn spam-detection

Last synced: about 1 month ago
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This is a spam email detection model

• Host: GitHub
• URL: https://github.com/vansh-khaneja/spam-email-detection
• Owner: vansh-khaneja
• License: mit
• Created: 2023-11-27T12:33:54.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-01T06:16:44.000Z (about 1 year ago)
• Last Synced: 2024-01-25T00:12:55.523Z (11 months ago)
• Topics: machine-learning, naive-bayes-classifier, scikit-learn, spam-detection
• Language: Jupyter Notebook
• Homepage:
• Size: 323 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Spam-Email-Detection

## Accuracy Rate :- 98.46%

This Spam Email detection model is able to filter out the mails whether they are spam or not.

The dataset used for training model is taken from kaggle :- https://www.kaggle.com/datasets

The project is created by using the Naive Bayes MultinomailNB Algorithm from sklearn library in Pyhton.

For testing the model you may use python file spam_email_detector.py where u can give the mail content in the input and you will get the result as output.

Also give a look at the jupyter notebook spam_detector.ipynb for better understanding using statistics and graphs

## Steps

1) Sorting out data with contvectorizer. 

2) Spliting data into training data and testing data. 

3) Testing data on various ML models to find out the best one. 

4) Training model with the train datset. 

5) Testing model to check accuracy with the test data. 

6) Testing the trained model with custom mails.