Spam SMS Classification Dataset

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Spam SMS Classification Dataset

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Spam SMS Classification

Use Case

Spam SMS Classification

Description

Explore our comprehensive Spam SMS Classification Dataset designed for NLP and machine learning research. Enhance your spam detection models with labeled SMS data.

Description:

This dataset focuses on classifying SMS messages as spam or not, based on natural language processing (NLP) techniques. It consists of a collection of SMS messages labeled for mobile spam detection, aiding research in filtering unsolicited or harmful messages.

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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.