Finding Donors for CharityML

This project pertains to a Supervised Learning Classification problem wherein people likely to donate for a cause are identified. The existing premise is that people earning more than $50K annually are more likely to donate. This project makes use of Machine Learning to identify such people.
After these people are identified, they are tageted with letters urging them to donate.

Dependencies

This project requires Python 3.x 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

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)