Build an algorithm to best identify potential donors of CharityML

We developed a model that will predict the likelihood that a given employed citizens of CA as a potential donor of a fictitious charity organization, Charity ML.

Finding doners for charityML

Finding donors using supervised learning

CharityML is a fictitious charity organization that was established to provide financial support for people eager to learn machine learning.

This project help identify people who are most likely to donate to CharityML(fictious charity organization)

Supervised Learning - Finding Donors for CharityML

Finding Donor for CharityML - Machine Learning Nanodegree from Udacity

Finding Donors for CharityML using supervised learners.

Udacity Machine Learning Nanodegree Supervised Learning Project

Employing several supervised algorithms to accurately model individuals' income.

Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.

Machine Learning Engineer Nanodegree, Supervised Learning, Finding Donors for CharityML

Project-1 of Udacity's Introduction to Machine Learning with TensorFlow Nanodegree. "Finding Donors for CharityML" is a Supervised Learning Project with Scikit-learn that aims to build a model that accurately predicts whether an individual earns more than $50,000

Applied 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.

Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.