Finding Donors for Charity - 1st project for Udacity's Data Science 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. After nearly 32,000 letters sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail.
The 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.
Here, I first investigate the factors that affect the likelihood of charity donations being made. Then, I use a training and predicting pipeline to evaluate the accuracy and efficiency/speed of three supervised machine learning algorithms (SVM, Adaboost, KNN). I then proceed to fine tune the parameters of the algorithm that provides the highest donation yield (while reducing mailing efforts/costs). Finally, I also explore the impact of reducing number of features in data.
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.
The main code for this project is located in the finding_donors.ipynb notebook file. Additional supporting code for visualizing the necessary graphs can be found in visuals.py.
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.ipynbor
jupyter notebook finding_donors.ipynbThis 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)