Finding Donors for CharityML Project

Project Description:

This is the first project for Data Scientist Nanodegree Program. In this project, I used sklearn and supervised learning techniques on data collected for the U.S. census to help a fictitious charity organization identify people most likely to donate to their cause. I have used three supervised machine learning algorithms (Logistic Regression, K-Nearest Neighbors , K Random Forest).

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.

Featureset Exploration:

• age: continuous.
• workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
• 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: continuous.
• marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
• 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: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
• race: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other.
• sex: Female, Male.
• capital-gain: continuous.
• capital-loss: continuous.
• hours-per-week: continuous.
• 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.

Software and Libraries:

This project uses the following software and Python libraries:

• Python
• NumPy
• pandas
• scikit-learn (v0.17)
• Matplotlib

Instructions:

This project contains three files that you will need to use:

• finding_donors.ipynb: This is the main file.
• census.csv: The project dataset.
• visuals.py: This Python script provides supplementary visualizations for the project. Do not modify.