Supervised Learning

Project: Finding Donors for CharityML

Problem

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 were 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 us on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail.
Our goal here is 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.

Models choice

After an exploration and a preparation (transforming skewed continuous features, normalizing numerical features, preprocessing) of the data, we choose three models to evaluate.
Commonly, Decision Trees are weak learners. They used to have high variance and low bias. Findind a way to optimize both variance and bias requires to use Ensemble models. So we test Bagging to mitigate the variance and Boosting for the bias. First we choose Random Forest which is an improvement over bagging. Regarding the boosting, AdaBoost and GradientBoosting have two different ways on working on the weak learners, so we try both of them.

Implementation Results

Performance metrics

GradientBoosting is the most approriate model for the task of identifying individuals that make more than $50,000

Tuning Results

Metric	Unoptimized Model	Optimized Model
Accuracy Score	0.8630	0.8678
F-score	0.7395	0.7469

Extracting feature importance

We implemented a feature importance extraction using the GradientBoosting Classifier.

Install

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

We recommend to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

The code is provided in the finding_donors.ipynb notebook file. The code included in visuals.py is meant to provide the visualizations created in the notebook.

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)