hamatoatef / repository

finding_donors1

# Machine Learning Engineer Nanodegree ## Project 3: Finding Donors for CharityML ### Project Description This is the 3rd project for the Machine Learning Engineer Nanodegree. 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. Here, I first investigate the factors that affect the likelihood of charity donations being made. Then, I use a training and predicting pipeline to evaluate the accuracy and efficiency/speed of three supervised machine learning algorithms (GaussianNB, SVC, Adaboost). I then proceed to fine tune the parameters of the algorithm that provides the highest donation yield (while reducing mailing efforts/costs). Finally, I also explore the impact of reducing number of features in data. ### Install This project requires **Python 2.7** and the following Python libraries installed: - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org) - [matplotlib](http://matplotlib.org/) - [scikit-learn](http://scikit-learn.org/stable/) You will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html) We recommend students install [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. ### Code The main code for this project is located in the `finding_donors.ipynb` notebook file. Additional supporting code for visualizing the necessary graphs can be found in `visuals.py`. Additionally, the `Report.html` file contains a snapshot of the main code in the jupyter notebook with all code cells executed. ### 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: ```bash ipython notebook finding_donors.ipynb ``` or ```bash jupyter notebook finding_donors.ipynb ``` This will open the iPython Notebook software and project file in your browser. ### 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](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf), with the original dataset hosted on [UCI](https://archive.ics.uci.edu/ml/datasets/Census+Income). **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)