Employing several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census to construct a model that accurately predicts whether an individual makes more than $50,000, This sort of task can arise in a non-profit setting, where organizations survive on donations.

You can see my implementation and report Here.

In this project, you will apply 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. You will first explore the data to learn how the census data is recorded. Next, you will apply a series of transformations and preprocessing techniques to manipulate the data into a workable format. You will then evaluate several supervised learners of your choice on the data, and consider which is best suited for the solution. Afterwards, you will optimize the model you've selected and present it as your solution to CharityML. Finally, you will explore the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given.

My project was reviewed by a Udacity reviewer against the Finding Donors for CharityML project rubric.

• The finding_donors.ipynb notebook file with all questions answered and all code cells executed and displaying output.
• An HTML export of the project notebook with the name report.html. This file must be present for your project to be evaluated.