gggff123/spam_detection_model

This project is a small, pre-built spam detection tool aimed squarely at complete beginners who want to learn the basics of data science and machine learning. Rather than asking someone to train a model from scratch, it hands over a model that already works, along with the raw source code, so a newcomer can see both a finished result and how it was made. The model looks at a text message and predicts whether it is spam or a normal message, which the project calls ham. It also reports how confident it is in that prediction, giving separate probability scores for spam and for ham rather than just a single yes or no answer. Under the hood it relies on common, well-established machine learning tools: scikit-learn for the model itself, joblib for saving and loading it, and numpy and pandas for handling data. The repository is organized simply, with one file holding the raw code used to build the model and a separate short script showing how to actually load and run the finished model. To use it, someone downloads two files from the project's releases section, a vectorizer that turns text into numbers the model can understand, and the trained model itself, then loads both with a few lines of Python. Typing a message in and running the script will print whether it thinks the message is spam, along with the probability behind that call. The author describes this explicitly as a fun hobby project meant to help beginners build something real they can point to, rather than a production-ready spam filter. The training data came from an existing spam messages dataset hosted on Hugging Face, credited to its original creator. The project is released under the MIT license.