Skip to content

Supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent to ask for donations.

Notifications You must be signed in to change notification settings

wiflore/Supervised-Learning-Finding-Donors-for-CharityML

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Supervised Learning: Finding Donors for CharityML

Project Overview

In this project, I applied supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML identify people most likely to donate to their cause.

  • I first explored the data to learn how the census data is recorded.
  • Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format.
  • Then I evaluated several supervised learners and considered which is best suited for the solution.
  • Afterwards, I optimized the model
  • Finally, I explored the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given.

Project Highlights

Things that I applied to complete 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.

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)

Code

The code is provided in the finding_donors.ipynb notebook file.

Install

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.

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.

About

Supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent to ask for donations.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published