Helping non-profits find potential donors using supervised learning techniques.
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LICENSE.md
README.md
census.csv
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README.md

Finding Donors for CharityML

Project Overview

In this project, we will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. We will first explore the data to learn how the census data is recorded. Next, we will apply a series of transformations and preprocessing techniques to manipulate the data into a workable format. We will then evaluate several supervised learners of our choice on the data, and consider which is best suited for the solution. Afterwards, we will optimize the model we've selected and present it as our solution to CharityML. Finally, we will explore the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given. predicted selling price to our statistics.

Description

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. Your goal will be 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.

Things learned by completing this project:

  • How to identify when preprocessing is needed, and how to apply it.
  • How to establish a benchmark for a solution to the problem.
  • What each of several supervised learning algorithms accomplishes given a specific dataset.
  • How to investigate whether a candidate solution model is adequate for the problem.

Software Requirements

This project requires Python 2.7 and the following Python libraries installed:

You will also need to have software installed to run and execute a Jupyter Notebook

It's recommended to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

This project contains three files:

  • finding_donors.ipynb: This is the main file where you will be performing your work on the project.
  • census.csv: The project dataset. You'll load this data in the notebook.
  • visuals.py: A Python file containing visualization code that is run behind-the-scenes. Do not modify.

In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook finding_donors.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens.

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 Jupyter 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)