In this project, I applied supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML identify people most likely to donate to their cause.
- I first explored the data to learn how the census data is recorded.
- Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format.
- Then I evaluated several supervised learners and considered which is best suited for the solution.
- Afterwards, I optimized the model
- Finally, I explored the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given.
Things that I applied to complete this project:
- How to identify when preprocessing is needed, and how to apply it.
- How to establish a benchmark for a solution to the problem.
- What each of several supervised learning algorithms accomplishes given a specific dataset.
- How to investigate whether a candidate solution model is adequate for the problem.
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.
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)
Target Variable
income
: Income Class (<=50K, >50K)
The code is provided in the finding_donors.ipynb
notebook file.
This project requires Python 3.x and the following Python libraries installed:
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.
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.