Machine learning project to choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Project's goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000.
This project requires Python 2.7 and the following Python libraries installed:
NumPy
Pandas
matplotlib
scikit-learn
You will also need to have software installed to run and execute an iPython Notebook
We recommend students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
Template 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. Note that the code included in visuals.py
is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
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.ipynb
or
jupyter notebook finding_donors.ipynb
This 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.
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)