This project is only for educational purposes. I did it while I was following the Udacity DataScience nanodegree
.
It is a supervised learning classification problem in which you work for a fictional entity named CharityML.
The goal is to find, among a given dataset, if people have a sufficient income and then classify them as a potential donor.
This project requires Python 3.6 and the following Python libraries installed:
- NumPy (I used 1.16.4)
- Pandas (I used 0.24.2)
- matplotlib (I used 3.1.0)
- scikit-learn (I used 0.21.2)
- iPython Notebook to run the notebook
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. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. 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 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. 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)