SMS Spam Filtering Using Python AI/ML

SMS Spam Filtering using Python AI/ML is a project designed to automatically classify and filter unwanted spam messages from legitimate texts. By leveraging machine learning algorithms, this system can accurately identify and segregate spam messages, improving the user experience by reducing clutter and potential security threats.

Key Features:

• Spam Detection: Identifies and categorizes SMS messages as spam or not spam based on their content.
• Machine Learning Models: Utilizes various models such as Naive Bayes, Support Vector Machines (SVM), and deep learning techniques for accurate classification.
• Preprocessing: Involves text cleaning and preprocessing steps like tokenization, stemming, and removal of stop words to enhance model performance.
• Feature Extraction: Converts text messages into numerical features using techniques such as Bag of Words (BoW), TF-IDF, or word embeddings.

Technologies Used:

• Python Libraries: scikit-learn for implementing traditional machine learning models, TensorFlow or PyTorch for deep learning models, and NLTK or spaCy for text preprocessing.
• Data Processing: Utilizes datasets containing labeled SMS messages to train and evaluate the model. Common datasets include the SMS Spam Collection dataset.

Benefits:

• Enhanced Security: Protects users from fraudulent and potentially harmful messages.
• Improved User Experience: Reduces the volume of unwanted messages, making it easier for users to manage their texts.
• Adaptability: The system can be trained to adapt to new types of spam and evolving messaging trends.

Implementation Steps:

1. Data Collection: Gather and preprocess SMS message data, including both spam and non-spam examples.
2. Text Preprocessing: Clean and prepare text data for analysis, including tokenization and feature extraction.
3. Model Training: Train machine learning models using labeled data to distinguish between spam and non-spam messages.
4. Evaluation: Assess model performance using metrics such as accuracy, precision, recall, and F1-score.
5. Deployment: Implement the model in a real-time application to filter incoming SMS messages.

By integrating AI/ML techniques, this project offers an efficient solution for SMS spam filtering, leveraging advanced algorithms to improve message management and user security.