SMS Spam Detection using TensorFlow in Python

SMS spam detection is a crucial NLP application that filters malicious or unwanted text messages. Using TensorFlow, a robust spam detection model can be built by preprocessing SMS data, extracting features with methods like TF-IDF or word embeddings, and training a neural network with layers such as embeddings, dense connections, and dropout for binary classification. The model's performance is evaluated using metrics like accuracy, precision, recall, and F1-score, addressing challenges like imbalanced datasets and evolving spam techniques. Deployment options include integrating the model into SMS filtering systems or phishing protection tools.

Key steps include gathering a labeled dataset, cleaning and tokenizing text, transforming it into numerical features, and optimizing the model with techniques like hyperparameter tuning. Despite challenges such as short text lengths and the dynamic nature of spam patterns, this project demonstrates the power of machine learning in enhancing user experience by blocking spam effectively. 

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