spam sms

Developed a Machine Learning model to detect and classify spam messages in SMS datasets.

Utilized Natural Language Processing (NLP) techniques for text preprocessing, including tokenization, stop-word removal, and TF-IDF vectorization.

Implemented and compared various algorithms such as Logistic Regression, Naive Bayes, and Support Vector Machine (SVM) to enhance model accuracy.

Used Python and libraries like Scikit-learn, Pandas, and NumPy for data handling and model training.

Achieved an accuracy of [insert accuracy]% by optimizing hyperparameters and evaluating performance through cross-validation.