In this project helps with supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. It first explores the data to learn how the census data is recorded. Next, it applies apply a series of transformations and preprocessing techniques to manipulate the data into a workable format.

Project also evaluates several supervised learners on the data, and consider which is best suited for the solution. Afterwards, it also optimizes the model that was selected and present the solution to CharityML.

Finally, it explores the chosen model and its predictions under the hood, to see how well it's performing when considering the data it's given.

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

software required to be installed to run and execute an iPython Notebook

Recommended to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.