https://github.com/jonad/finding_donors

Predicting income with UCI Census Income Dataset using supervised machine learning algorithms
https://github.com/jonad/finding_donors

numpy pandas scikit-learn scikitlearn-machine-learning

Last synced: about 1 month ago
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Predicting income with UCI Census Income Dataset using supervised machine learning algorithms

• Host: GitHub
• URL: https://github.com/jonad/finding_donors
• Owner: jonad
• License: mit
• Created: 2017-01-26T23:48:22.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-11-28T22:45:14.000Z (about 7 years ago)
• Last Synced: 2024-11-07T06:27:59.544Z (3 months ago)
• Topics: numpy, pandas, scikit-learn, scikitlearn-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 757 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Supervised Learning

## Project: Finding Donors for CharityML

### Install

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

- [NumPy](http://www.numpy.org/)

- [Pandas](http://pandas.pydata.org)

- [matplotlib](http://matplotlib.org/)

- [scikit-learn](http://scikit-learn.org/stable/)

You will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html)

We recommend students install [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. 

### Run

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:

```bash

ipython notebook finding_donors.ipynb

```  

or

```bash

jupyter notebook finding_donors.ipynb

```

### Data

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](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf), with the original dataset hosted on [UCI](https://archive.ics.uci.edu/ml/datasets/Census+Income).

**Features**

- `age`: Age

- `workclass`: 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 completed

- `marital-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 Gains

- `capital-loss`: Monetary Capital Losses

- `hours-per-week`: Average Hours Per Week Worked

- `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)