SMS-Spam-filtering-An-implementation-using-Python-and-Scikit-learn

Spam is becoming a growing concern for SMS users around the world. SMS spam can easily target and impact users without deception if the user has a limited plan and the message incurs a fee. This study creates an SMS spam filter by using machine learning algorithms to detect spam. We will use a natural language toolkit (NLTK) for text processing, Term Frequency-Inverse Document Frequency (TF-IDF) for the conversion of text data, and Logistic Regression, Naïve Bayes, Support Vector Machines (SVM) for the text classification machine learning algorithms. In addition to determining accuracy, we will use a classification matrix to examine the results based on the parameter of precision and use an AUC (Area Under the Curve) ROC (Receiver Operating Characteristics) curve to measure the overall performance of each model.