Introduction

In this project, we used sklearn's supervised learning techniques on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause.

Things I've learned doing this project:

1. How to identify when preprocessing is needed, and how to apply it.

2. How to establish a benchmark for a solution to the problem.

3. What each of several supervised learning algorithms accomplishes given a specific dataset.

4. How to investigate whether a candidate solution model is adequate for the problem.