This project demonstrates the training of a deep learning model capable of detecting spam messages. The dataset used for this project is the SMS Spam Collection Dataset from Kaggle.
Spam detection is a critical task in the field of communication, aimed at filtering out unwanted messages. This project leverages NLP techniques to classify SMS messages as spam or ham (non-spam).
The dataset used in this project is the SMS Spam Collection Dataset from Kaggle. It contains a collection of SMS messages that have been labeled as either spam or ham.
The model used in this project is a Convolutional neural network (CNN) with a trainable embedding layer.
To run this project, you need to have Python installed along with the required libraries. You can install the necessary dependencies using the following command:
pip install -r requirements.txtTo train and test the model, run the main.ipynb notebook. This notebook contains all the steps from data preprocessing, model training, and evaluation.
The performance of the model can be evaluated using metrics such as accuracy, precision, recall, and F1-score. These metrics help in understanding the effectiveness of the spam detection model.
Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.