- Introduction
The objective of this project is to develop a machine learning model using TensorFlow to detect spam emails. Spam detection is a critical task in email filtering systems, as it helps identify and filter out unwanted or potentially harmful emails. In this report, we outline the steps taken to prepare the dataset, build a TensorFlow model, evaluate its performance, and analyze the model coefficients.
- Dataset
The dataset used for this task is the SMS Spam Collection Dataset. It contains text messages labeled as spam or ham (non-spam). The dataset was preprocessed to remove any missing values and split into training and test sets. The text messages were tokenized and padded to ensure uniform length for model input.
- Model Architecture
The TensorFlow model was constructed using a deep learning architecture suitable for text classification tasks. The model architecture consists of an embedding layer followed by one or more dense layers. The embedding layer converts text data into dense vector representations, while dense layers with dropout regularization prevent overfitting.
- Model Training and Evaluation
The model was trained using the training data and evaluated on the test set. The training process involved iterating over the dataset for a fixed number of epochs while optimizing a chosen loss function (binary cross-entropy) using the Adam optimizer. During evaluation, metrics such as accuracy, precision, recall, and F1-score were computed to assess the model's performance.
- Results
The trained TensorFlow model achieved satisfactory performance in detecting spam emails, with accuracy, precision, recall, and F1-score indicating its effectiveness. The confusion matrix and classification report provided detailed insights into the model's performance across different classes (spam and non-spam).
- Conclusion
In conclusion, the TensorFlow model demonstrated the capability to effectively identify spam emails based on their textual content. By leveraging deep learning techniques, the model can be deployed in email filtering systems to automatically classify incoming emails as spam or non-spam, thereby enhancing email security and user experience.
- Future Work
Future work may involve experimenting with different model architectures, hyperparameter tuning, and incorporating additional features such as email metadata or sender information to further improve the model's performance. Additionally, deploying the model in a production environment and monitoring its performance over time would be essential for real-world deployment.
Overall, the spam detection model developed using TensorFlow presents a promising solution to the challenge of identifying and filtering out unwanted emails, contributing to the improvement of email security and user privacy.
Dataset used:https://www.kaggle.com/code/akanksha496/spam-detection-using-tensorflow/input