Data Scientist Nanodegree, Term 1, Project 1: Finding Donors for CharityML

Background

This is a project I completed for my Udacity Data Scientist Nanodegree. What follows in this README is paraphrased and sometimes directly quoted from the project documentation, with my own edits dispersed throughout for clarity or elaboration.

Purpose

CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought me on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. My goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

Data

The modified Census dataset consists of approximately 32,000 data points, with each record having 13 features. This dataset is a modified version of the dataset published in the paper "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", by Ron Kohavi. You may find this paper online, with the original dataset hosted on UCI.

Features

1. age: Age
2. workclass: Working Class (Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked)
3. education_level: Level of 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)
4. education-num: Number of educational years completed
5. marital-status: Marital status (Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse)
6. occupation: Work 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)
7. relationship: Relationship Status (Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried)
8. race: Race (White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black)
9. sex: Sex (Female, Male)
10. capital-gain: Monetary Capital Gains
11. capital-loss: Monetary Capital Losses
12. hours-per-week: Average Hours Per Week Worked
13. native-country: 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 Variable

income: Income Class (<=50K, >50K)