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Project: Finding Donors for CharityML. The project is part of Udacity 'Introducton to Machine Learning with Tensorflow' excersises.

In this project supervised learning techniques are applied on data collected on U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. The project includes

• exploration of the input data,
• preprocessing and transforming the input data to be suitable for supervised learning methods
• alternative supervised learning techniques are assessed, and three most suitable for the data and the problem are selected
• selected models are applied to predict the income class based on other sociodemographics features
• each of the models performance is evaluated and the most suitable supervised learning model is selected
• for the selected model hyperparameter optimization is conducted to optimze model performance
• final model prediction performance is evaluated considering the given data.

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have Juypyter software installed to run and execute an iPython Notebook

• finding_donors.ipynb Jypter notebook file with the main code and analysis
• visuals.py Python scripts to visualize intermeidate results
• census.csv U.S. census data (see Data for detailed description)

In a terminal or command window run the following commands:

ipython notebook finding_donors.ipynb

This will open the iPython Notebook software and project file in your browser.

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)