This project is a spam filter that uses machine learning techniques to classify emails as either spam or non-spam. It aims to accurately identify and filter out unwanted spam emails, improving the user's email experience.
- Training the model: The project provides a mechanism to train the spam filter model using a labeled dataset of spam and non-spam emails.
- Evaluation: The model's performance can be evaluated using various metrics such as accuracy, precision, recall, and F1 score.
- Prediction: Once trained, the model can be used to predict whether a given email is spam or not.
- Clone the repository:
git clone https://github.com/szpajak/NLP_Spam_Detector.git - Install the required dependencies:
pip install -r requirements.txt
- Prepare the dataset: Collect a labeled dataset of spam and non-spam emails.
- Train the model: Run the training script to train the spam filter model using the dataset.
- Evaluate the model: Use the evaluation script to assess the model's performance.
- Predict spam emails: Apply the trained model to classify new emails as spam or non-spam.