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Machine Learning Engineer Nanodegree

Supervised Learning

Project: Finding Donors for CharityML

Project Overview

In this project, you 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.

  • You will first explore the data to learn how the census data is recorded.
  • Next, you will apply a series of transformations and preprocessing techniques to manipulate the data into a workable format.
  • You will then evaluate several supervised learners of your choice on the data, and consider which is best suited for the solution.
  • Afterwards, you will optimize the model you've selected and present it as your solution to CharityML.
  • Finally, you 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.

Project Highlights

This project is designed to get you acquainted with the many supervised learning algorithms available in sklearn, and to also provide for a method of evaluating just how each model works and performs on a certain type of data. It is important in machine learning to understand exactly when and where a certain algorithm should be used, and when one should be avoided.

Things you will learn 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.

Project Evaluation

Your project will be reviewed by a Udacity reviewer against the Finding Donors for CharityML project rubric. Be sure to review this rubric thoroughly and self-evaluate your project before submission. All criteria found in the rubric must be meeting specifications for you to pass.

Software Requirements

This project uses the following software and Python libraries:

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

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

Template code is provided in the finding_donors.ipynb notebook file. You will also be required to use the included visuals.py Python file and the census.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.

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)