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

This document presents my complete solutions to Exercises 8.1, 8.2, 8.3, and 8.7 from Applied Predictive Modeling by Kuhn and Johnson.

Juan M. Jimenez - Jose M. Silva Analitica de Negocios PUJ

This interactive infographic presents the complete workflow of a Machine Learning (ML) project focused on predicting the risk of Stroke (Cerebrovascular Accident).

Integrantes: Deysi Herrera y Alejandra Suarez

Solutions to problem set 2 for experimental psychology methods class.

References: "Understanding Cost of Living and Purchasing Power: What the Numbers Really Tell Us" - Eutim P "Cost of Living Index by County 2024 Mid-Year" - Numbeo "Cost of Living Index by City 2025 Mid-Year " - Numbeo