A machine learning research project and paper analyzing the efficiency of different ML algorithms using evaluation metrics and drawing a comparison between them. The data is split into training data and testing data in an 80:20 ratio in accordance with the Pareto Principle. The algorithms analyzed in this project are: SVM, Random Forest, Decision Trees and Naive Bayes.
Type the following command to run the program:
python algorithm.pyThe output will be something similar to:
Start time: 2017-01-01 15:04:42
[ ***Processing data*** ]
##############################################################
[ ***Predicting data*** ]
Error margin for Naive Bayes: 3.30
Error margin for Random Forest: 1.13
Error margin for Decision Tree: 1.23
End time: 2017-01-01 15:05:59
For bugs, questions and discussions please use the Github Issues.