This Streamlit app is a robust email spam detection tool that leverages different machine learning models to predict whether an email is spam or not. Users can choose from various models, input email text directly, or upload a CSV file containing emails for bulk prediction.
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Link to the website: https://email-spam-detector-m.streamlit.app/
- Model Accuracies: Display the accuracy scores of different machine learning models.
- Predictions: Input email text or upload a CSV file for spam predictions.
- User-Friendly Interface: Streamlit UI ensures an intuitive and easy-to-use experience.
- python3
- git
- pip
git clone https://github.com/pankil-soni/email-spam-detector.git
cd email-spam-detector
pip install -r requirements.txt
streamlit run app.py
- Select "Text" as the input type.
- Enter the email text in the provided text area.
- Click the "Predict" button to get the spam prediction.
- Select "CSV File" as the input type.
- Upload a CSV file with a column named 'Email.'
- The app will predict spam for each email and display the results.
- Logistic Regression
- Random Forest Classifier
- Support Vector Classifier (SVC)
- Complement Naive Bayes
This project is designed for email spam detection, offering flexibility in model selection and input types. It provides an efficient way to analyze single emails or process bulk predictions from CSV files.
Contributions are welcome! Please fork the repository and create a pull request.
This project is created by pankil soni :
- github: https://github.com/pankil-soni
- linkedin : https://www.linkedin.com/in/pankil-soni-5a0541170/
- email : pmsoni2016@gmail.com