This project is a machine learning-based application designed to classify messages and emails as either spam or ham (not spam).
- Spam Classification: Predicts whether a given SMS or email message is spam or not.
- Machine Learning Model: Utilizes a trained model for accurate predictions.
- Web Interface: Provides a user-friendly interface for inputting messages and viewing results.
1 : Clone the Repository: < git clone https://github.com/Stonebanks-js/SMS-and-Email-spam-detection-system-.git >
2 : Navigate to the Project Directory: < cd SMS-and-Email-spam-detection-system- >
3 : Create a Virtual Environment: < python -m venv venv >
4 : On Windows: < venv\Scripts\activate >
: On linux : < source venv/bin/activate >
5 : Install Dependencies: < pip install -r requirements.txt >
1 : Run the Application: < python app.py >
2 : Access the web interface : < Open your web browser and navigate to http://127.0.0.1:5000/. >
3 : Classify a Message: :
- Enter the text of the SMS or email message into the input field.
- Click the "Predict" button to determine if the message is spam or ham.
The model is trained on a dataset containing labeled SMS messages. The dataset is included in the repository as spam.csv.
The model was trained using a Jupyter Notebook (SMS_SPAM_CLASSIFIER.ipynb) included in the repository. It utilizes a machine learning algorithm to achieve accurate spam detection.
- Python 3.x
- Flask
- Pandas
- Scikit-learn
- Jupyter Notebook
All required Python packages are listed in requirements.txt.
This project is licensed under the MIT License.
- Connect with me:
- GitHub: Stonebanks-js
- Email: [email protected]
- Linktree: stonebanks.js
- Instagram: st0nebanks.js
- SMS Spam Detection Tutorial
- Official Python Documentation: Python.org
- Flask Documentation: Flask.palletsprojects.com
- Scikit-learn Documentation: scikit-learn.org