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https://github.com/gauravg-20/spam-email-detection-using-multinomialnb

The app takes email input in text format from the user and accurately classifies it either as spam or ham (not spam) with an overall accuracy of 95%. You can access the app using the link below.
https://github.com/gauravg-20/spam-email-detection-using-multinomialnb

jupyter-notebook multinomial-naive-bayes python spam-email-detection streamlit-webapp

Last synced: 12 days ago
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The app takes email input in text format from the user and accurately classifies it either as spam or ham (not spam) with an overall accuracy of 95%. You can access the app using the link below.

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README 

        
# Spam-Email-Detection-using-MultinomialNB



## Overview



This project implements a spam email classifier using the Multinomial Naive Bayes algorithm. The classifier analyzes email subjects to differentiate between spam and ham (non-spam) messages. By leveraging the CountVectorizer for text processing and Multinomial Naive Bayes for classification, this project offers an efficient solution for email filtering.



## Deployed App



I have deployed the Spam Email Detector App, and it is accessible via the following link: [Spam Email Detector](https://spam-email-detection-xkfbkgmxafoxvaaprmxlnr.streamlit.app/).






## How it Works



1. **Text Processing:**

   - Email subjects are processed using the CountVectorizer, which converts text into a matrix of token counts. This step involves creating a vocabulary of words present in the dataset.

   

2. **Training the Model:**

   - The dataset, comprising labeled spam and ham email subjects, is split into training and testing sets. The Multinomial Naive Bayes classifier is trained on the training data to learn the patterns and characteristics of spam emails.

   

3. **Classification:**

   - When a new email subject is provided, the trained classifier uses the CountVectorizer to convert it into token counts and predicts whether it's spam or ham based on the learned patterns.



## Dataset Used



The classifier is trained and tested on a [**Spam Mails Dataset**](https://www.kaggle.com/datasets/venky73/spam-mails-dataset) containing labelled email subjects, with indications of whether they are spam or ham.



Dataset Description:



![Details of the dataset](https://github.com/GauravG-20/Spam-Email-Detection/blob/main/dataset_description.png)



## Model Accuracy



The classifier's accuracy is a crucial metric to evaluate its performance. After training, the model's accuracy is determined using the testing dataset. This **accuracy score was 95%**.



## Libraries and Tools Used



- **Python Libraries:** pandas, numpy, CountVectorizer, MultinomialNB from sklearn

- **Data Processing:** CountVectorizer is employed to convert text documents into token counts, creating the feature matrix.

- **Model Training:** Multinomial Naive Bayes classifier is used for training the model.



## Getting Started



1. **Clone the Repository:**



     ```

     git clone https://github.com/GauravG-20/Spam-Email-Detection-using-MultinomialNB.git

     cd Spam-Email-Detection-using-MultinomialNB

     ```

3. **Install Dependencies:**  

      

    ```

    pip install pandas numpy scikit-learn streamlit

    ```

3. **Run the Streamlit App:**  



     ```

     streamlit run app.py

     ```



## Contribution Guidelines



Contributions from the open-source community are welcome. To contribute, follow these steps:



1. Fork the repository on GitHub.

2. Create a new branch with a descriptive name.

3. Make your changes and commit them with clear comments.

4. Push your changes to your fork.

5. Open a pull request, explaining the changes made.



## License



This project is licensed under the [MIT License](LICENSE.md) - see the [LICENSE.md](LICENSE.md) file for details.