This is the 3rd project for the Machine Learning Engineer Nanodegree. In this project, I used sklearn and supervised learning techniques on data collected for the U.S. census to help a fictitious charity organization identify people most likely to donate to their cause.

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 (GaussianNB, SVC, Adaboost). 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 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

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. Additionally, the Report.html file contains a snapshot of the main code in the jupyter notebook with all code cells executed.

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.

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)