This is one of the projects completed and passed successfully with Udacity's 'Introduction to Machine Learning with PyTorch' Nanodegree.
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning.
My goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent to ask for donations.
*Installations
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
I recommend installing Anaconda.
*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:
ipython notebook finding_donors.ipynb or
jupyter notebook finding_donors.ipynb This will open the iPython Notebook software and project file in your browser.
#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, with the original dataset hosted on UCI.
*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)