This final project in the Supervised Learning section, is part of Udacity's Machine Learning Nanodegree program. It is a good machine learning case study which requires in depth analysis and knowledge on ML techniques. I have gained good expertise on this approach. Congrats to me for finishing this project with great work on its implementation, I am so excited to do other projects in this Nanodegree!

In this project, I applied supervised learning techniques on data collected for the US census to help CharityML (a fictitious charity organization) identify groups of people that are most likely to donate to their cause. Finding potential donors for a charity involves analyzing data about the US population and grouping that population by similar interests/traits that can help identify likely donors. I get 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.

• Explore the data to learn how the census data is recorded.
• How to identify when preprocessing is needed, and how to apply it.
• Apply a series of transformations and preprocessing techniques to manipulate the data into a workable format.
• Evaluate several supervised learners on the data and consider which is best suited for the solution.
• Optimize the selected model and present it as a solution to CharityML.
• Finally, explore the chosen model and its predictions under the hood, to see how well it's performing.

This project uses the following software and Python libraries: • Python • NumPy • pandas • scikit-learn (v0.17) • Matplotlib You will also need to have software installed to run and execute a Jupyter Notebook.

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)

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.