Spam SMS Classification Dataset

Data Collection:

The dataset was gathered from publicly accessible sources, making it suitable for research purposes in mobile phone spam identification. It’s designed for use in text-based machine learning models.

Dataset Characteristics:

• Multivariate: Contains multiple features for analysis.
• Text-Based: Comprises SMS text messages for classification.

Subject Areas:

• Computer Science
• Machine Learning
• Deep Learning

Details:

• Structure: Each SMS is labeled as “spam” or “ham” (not spam) to train classification models.
• Applications: Ideal for building spam filters using machine learning algorithms like Naive Bayes, SVM, or neural networks.
• Preprocessing: NLP techniques such as tokenization, stop-word removal, and stemming can enhance model performance.
• Use Case: Beneficial for applications requiring automated message filtering, improving user experience in communication platforms.
• Ethical Considerations: Ensures fair use of personal data, promoting responsible AI practices.