Machine Learning Engineer Nanodegree

Project 3: Finding Donors for CharityML

Project Description:

Finding donors for CharityML which is a charity organization located in the heart of Silicon Valley. This is a project for the Udacity Nanodegree program. The goal is to use sklearn and supervised machine learning techniques on data from the U.S. Census to identify people who are most likely to donate.

Installation:

This project requires Python 2.7 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

and software installed to run and execute a Jupyter Notebook (https://jupyter.org/) or simply install Anaconda (https://www.anaconda.com/) which provides all the necessary libraries needed.

Running the Program:

Clone this repo to your computer. use cd command from the terminal to the directory where this repo is located and run the following command:

jupyter notebook

About the Data:

Featureset Exploration

• age: continuous.
• workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
• education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
• education-num: continuous.
• marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
• occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, - Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
• relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
• race: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other.
• sex: Female, Male.
• capital-gain: continuous.
• capital-loss: continuous.
• hours-per-week: continuous.
• native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

Target segment

Individuals whose income is more than 50K

References:

• Udacity's interpretation of the data