Email Spam Detection App Built on Logistic Regression

Spam filtering is one of the oldest applied text classification problems and still one of the best ways to learn the craft: the task is intuitive, the data is text, and mistakes in either direction have obvious costs. This project builds a working spam detector from classical, explainable ingredients. The intern trains a logistic regression classifier with scikit-learn on labeled email data, converting raw text into numerical features through vectorization and using pandas to manage the dataset throughout. A Streamlit interface lets users paste any email text and instantly see the verdict — spam or legitimate — together with the model's confidence, so borderline messages become teachable moments about probability thresholds. The intern experiments with preprocessing choices such as lowercasing and stopword handling, observing directly how each choice moves precision and recall. The project delivers a grounded introduction to text features, linear classifiers, and probabilistic outputs while producing a satisfying interactive artifact. The intern comes away able to explain the full text classification pipeline from raw string to decision — a foundational pattern that scales up to every modern language system.