Realizar la creacion de vectores, manipular vectores y utilizacion de operaciones aritmeticas.

testing for revealjs ,quarto presentation

Classifying emails as spam or ham using the SpamAssassin dataset with Naive Bayes and Logistic Regression. Includes text preprocessing, model comparison, and predictions on new messages.

Project Summary In this project, I built an email classifier using Naive Bayes and TF-IDF to automatically categorize emails into multiple categories. Dataset & Methods - Data: 3,200 personal Gmail messages (4 categories: Inbox, Promotions, Social, Updates) - Features: TF-IDF with 500 top terms - Model: Naive Bayes classifier (80/20 train/test split) Results - Overall Accuracy: 55% - Best Performance: Social emails (87%) - distinctive words like "liked", - commented", "tagged" - Lowest Performance: Inbox, Promotions, Updates (24-41%) - similar transactional vocabulary Key Findings -Category distinctiveness drives performance. - Social media emails have unique vocabulary, while promotional and transactional emails share similar language patterns, making them harder to distinguish.

Table 2 Descriptive and INferential Statistics