This project focuses on the detection of spam and ham (non-spam) emails using a machine learning logistic regression model. The implementation is done in a Jupyter Notebook using Google Colab.
Emails are a common means of communication, and distinguishing between spam and legitimate emails is crucial. This project uses a logistic regression model to classify emails as spam or ham based on certain features.
The machine learning model is implemented using the scikit-learn library in Python, with logistic regression as the chosen algorithm. The Jupyter Notebook is designed to guide you through the process of training, evaluating, and using the model for email classification.
To get started with this project, follow these steps:
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Clone the repository:
git clone https://github.com/mrnithish/Spam-Mail-Detection.git
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Open the Jupyter Notebook
Spam_Mail_Detection.ipynbin Google Colab. -
Follow the instructions in the notebook to execute the cells and train the model.
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Use the trained model to classify new emails as spam or ham.
The dataset used for training the model is available in the dataset folder. It includes labeled examples of spam and ham emails.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Contributions are welcome! If you'd like to contribute to this project, please follow our contribution guidelines.
- The scikit-learn developers for providing a powerful machine learning library.
- Google Colab for providing a free, cloud-based Jupyter Notebook environment.