In this project, we will predict the returns of each stock in the Dow Jones Index using Support Vector Regression and quantify the risk associated with each investment using a Capital Asset Pricing Model.

In this project, I will test several different supervised learning algorithms to develop a model that predicts whether an individual will make a donation to a nonprofit veterans' organization. A final KNN model was selected with an Area Under the Curve of 0.763.

Forecasting with ARIMA, ESM, and GARCH.

Introduction to Statistical Learning, Chapter 9 Exercises

Introduction to Statistical Learning, Chapter 8 Exercises

Introduction to Statistical Learning, Chapter 7 Exercises

Introduction to Statistical Learning, Chapter 6 Exercises

We will compare Support Vector Machines and Logistic Regression to develop a predictive model that can identify which customers to target in a new direct marketing campaign with 71.6% sensitivity.

Introduction to Statistical Learning, Chapter 5 Exercises

Introduction to Statistical Learning, Chapter 4 Exercises

Introduction to Statistical Learning, Chapter 3 Exercises

A Logistic Regression model was used to predict whether or not a customer would subscribe to a long-term bank deposit with 73.4% accuracy. Model selection and validation were performed using Akaike and Bayesian Information Criterion.

Introduction to Statistical Learning, Chapter 2 Exercises