Using machine learning algorithms on the Census Income dataset

• Python 3.5 and up
• Sci-kit Learn 0.17.1 and up
• Numpy 1.13.1 and up
• Pandas 0.20.3 and up

data_preprocessing.py: Load, normalize, and process the train/test data.

train_and_test.py: Train a machine learning algorith (either naive-bayes, knn, decision tree, or svm) on the census income data to predict whether income exceeds $50k per year.

The data used is from the UCI Machine Learning Repository and can be found here. For the program to run properly make sure the train and test data are titled "train_data.txt" and "test_data.txt" respectively and are both in a folder titled "data" that is in the same directory as both python files.

From terminal/command prompt navigate to the directory where data_preprocessing.py, train_test.py, and data are located and run one of the following commands.

python -c naive_bayes

python -c decision_tree

python -c knn

python -c svm

Note: If no classifier is provided then it defaults to naive bayes.