This document provides guidelines for statistical and machine learning models. It recommends defining the goal by determining whether prediction or inference is needed. Supervised models are recommended if the response is present in the data, and unsupervised models if the response is not present. Regression is used for quantitative responses while classification is for qualitative responses. The document provides guidance on selecting specific algorithms based on characteristics of the data and problem. It also covers assessing model accuracy, improving models, and addressing trade-offs between flexibility, interpretability, bias, and variance.