This project, Machine Learning Project 5: SMS Spam Classification, is aimed at developing a machine learning model to automatically classify SMS messages as either Spam or Ham (non-spam). The core objective is to apply natural language processing (NLP) and machine learning techniques to filter out unwanted spam messages and keep inboxes clean. The project begins with data preprocessing, where the SMS text data is cleaned by removing non-alphanumeric characters, converting text to lowercase, and applying other necessary text-cleaning steps. The processed text is then converted into numerical features using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization, which helps in transforming the raw text into a format that machine learning models can understand.

For the classification task, the project utilizes Logistic Regression, a powerful and simple machine learning algorithm, to distinguish between spam and ham messages. The model is evaluated on various performance metrics, including accuracy, precision, recall, and F1-score, to ensure its effectiveness in classifying messages. This project is built using Python, with libraries such as scikit-learn for machine learning tasks, pandas for data manipulation, and re for regular expression-based text processing. The model can be further expanded and deployed for real-world applications, such as integrating it into a messaging platform to classify incoming messages automatically.