With the increasing usage of mobile phones and text messaging, SMS spam has become a major concern for mobile phone users. Spam messages not only waste users’ time and money but also pose a security risk by tricking users into clicking on malicious links or disclosing sensitive information. Machine learning algorithms can provide a solution by automatically detecting and filtering spam messages.
In this project, we aim to use machine learning algorithms to analyze and detect spam messages in SMS datasets. We will use a range of features, such as text content, sender’s identity, and message frequency, to train the machine learning models. The proposed workflow for the SMS Spam Detection Analysis project includes the following steps:
- Data Collection and Preprocessing: We will collect a dataset of SMS messages, which includes both spam and non-spam messages. We will preprocess the dataset by cleaning and normalizing the text, removing stop words, and performing feature extraction.
- Feature Extraction: We will extract a set of features from the text messages, such as word frequency, character n-grams, and message length. We will also engineer new features, such as the presence of specific keywords or phrases, to improve the model’s performance.
- Model Training and Selection: We will train a set of machine learning models, such as logistic regression, decision trees, and support vector machines (SVMs), on the preprocessed dataset. We will evaluate the performance of each model using metrics such as precision, recall, and F1-score and select the best-performing model.
- Model Evaluation and Deployment: We will evaluate the performance of the selected model using cross-validation and backtesting techniques. We will then deploy the model to a cloud-based platform or mobile app, which can automatically detect and filter spam messages in real-time.
The expected outcomes of this project include a scalable and efficient machine learning algorithm for SMS spam detection, a comprehensive dataset of SMS messages, and a set of best practices and guidelines for applying machine learning algorithms to text message data. The project has numerous applications, including mobile phone security, marketing research, and customer relationship management. The insights gained from this project can also inform decision-making in other domains, such as email spam detection and social media analysis.
