Projects / Overview
Spam Email Classifier
Developed an automated natural language processing (NLP) and machine learning pipeline to classify incoming emails as spam or ham (legitimate). Designed to minimize security risks and clean inbox clutter, the platform processes raw email text through tokenization and TF-IDF feature extraction before passing data to trained supervised models for high-precision, real-time prediction.
Overview
Engineered an automated natural language processing and machine learning pipeline to classify incoming emails as spam or ham (legitimate). Built to filter unwanted communication and reduce security exposure, the system leverages statistical text vectorization and supervised classification models to process email body content and deliver high-precision predictions.
Key Features & Engineering
- Text Preprocessing & Tokenization: Designed an NLP pipeline to clean raw email text using stop-word removal, lowercasing, punctuation filtering, and stemming/lemmatization to normalize input data.
- Feature Extraction: Implemented TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to extract contextual keyword importance while dampening high-frequency non-informative words.
- Supervised Classification: Trained and evaluated supervised learning models—such as Naive Bayes, Logistic Regression, or Support Vector Machines (SVM)—optimizing hyper-parameters to maximize prediction accuracy and minimize false positives.
- Performance Metrics & Evaluation: Measured classification performance across precision, recall, F1-score, and confusion matrices to ensure legitimate emails are not misclassified as spam.