In this project, I employ several supervised algorithms to accurately predict an individual income using data collected from the 1994 U.S. Census.
We implement various testing procecures to choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data.
The primary goal of this implementation is 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.
Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can infer this value from other publically available features.
This project was submitted as part of the requisites required to obtain Machine Learning Engineer Nanodegree from Udacity.
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute an iPython Notebook
We recommend installing Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
The code is provided in the finding_donors.ipynb notebook file. You will also be required to use the included visuals.py Python file and the census.csv dataset file to complete your work.
In a terminal or command window, navigate to the top-level project directory finding_donors/ (that contains this README) and run one of the following commands:
ipython notebook finding_donors.ipynbor
jupyter notebook finding_donors.ipynbThis will open the iPython Notebook software and project file in your browser.
The modified census dataset consists of approximately 32,000 data points, with each datapoint 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.
age: Ageworkclass: Working Class (Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked)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)education-num: Number of educational years completedmarital-status: Marital status (Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse)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)relationship: Relationship Status (Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried)race: Race (White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black)sex: Sex (Female, Male)capital-gain: Monetary Capital Gainscapital-loss: Monetary Capital Losseshours-per-week: Average Hours Per Week Workednative-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)
income: Income Class (<=50K, >50K)
The present project constitutes intellectual work towards completion of Udacitys Machine Learning Engineer Nanodegree. You are free to modify and adapt the code to your needs, but please avoid using an exact copy of this work as your own to obtain credits towards any educational platform, doing so may imply plagiarism on your part.