Spam Message Detection Using NLP & Machine Learning

Spam Message Detection Using NLP & Machine Learning
I built a machine learning tool that uses NLP to label text messages as Spam or Not Spam. The tool works with the SMS Spam Collection Dataset from the UCI Machine Learning Repository.

Key Features
Used a labelled set of spam and normal SMS messages.

Did text cleaning and TF‑IDF Vectorization to turn words into numbers.

Ran the Multinomial Naive Bayes model to predict labels.

Separated the data into training and test parts to check performance.

Checked outcomes with accuracy, precision, recall and F1‑score.

Made a Streamlit app so you can try your own messages.

Loaded the model and TF‑IDF tool with Joblib.

Technologies Used
Python | NLP | Pandas | Scikit‑learn TF‑IDF | Multinomial Naive Bayes | Streamlit Joblib Dataset

SMS Spam Collection Dataset. UCI Machine Learning Repository
The model learns from this labelled data. Its guesses rely on the patterns from this set. Might not always be right, on brand‑new messages.

Project Outcome
I produced a ready‑to‑use text classifier that shows how NLP and machine learning can spot messages through a hands‑on web page