One of the most simple used supervised machine learning algorithm is a Decision tree.
Decision trees get to become very complex with very large data sets but that’s fine as its application lies where there is a small data set or we want to explain the customer/business how we landed upon a decision.
Decision trees are used to make a yes or no / 1 or 0 decision. In the case of UCI adult data set we want to predict if the individual has an income above or below 50K. Which is nothing but a factor variable.
Decision trees work amazing when all the explanatory variables are categorical and numerical.Rather numerical variables just cant be used here.Hence I have converted the entire data set into categorical variables.
To run the model i made use of the package RWeka in R which has the function j48 and achieved an accuracy of -> 83.8472 %
For the code and method please visit my GitHub link below