Build a data model - open to bidding

You are provided with a dataset that includes data of people who are interviewed for the Data Scientist job. The data is below and the fields are; each candidate’s level, their preferred language, whether they are active on Twitter and whether he/she has a PhD. The class label (last attribute) is either Yes (the candidate interviewed well) or No (the candidate interviewed poorly. inputs = [ ({'level':'Senior','lang':'Java','tweets':'no','phd':'no'}, False), ({'level':'Senior','lang':'Java','tweets':'no','phd':'yes'}, False), ({'level':'Mid','lang':'Python','tweets':'no','phd':'no'}, True), ({'level':'Junior','lang':'Python','tweets':'no','phd':'no'}, True), ({'level':'Junior','lang':'R','tweets':'yes','phd':'no'}, True), ({'level':'Junior','lang':'R','tweets':'yes','phd':'yes'}, False), ({'level':'Mid','lang':'R','tweets':'yes','phd':'yes'}, True), ({'level':'Senior','lang':'Python','tweets':'no','phd':'no'}, False), ({'level':'Senior','lang':'R','tweets':'yes','phd':'no'}, True), ({'level':'Junior','lang':'Python','tweets':'yes','phd':'no'}, True), ({'level':'Senior','lang':'Python','tweets':'yes','phd':'yes'},True), ({'level':'Mid','lang':'Python','tweets':'no','phd':'yes'}, True), ({'level':'Mid','lang':'Java','tweets':'yes','phd':'no'}, True), ({'level':'Junior','lang':'Python','tweets':'no','phd':'yes'},False) ] You are asked using this data to build a model identifying which candidates will interview well? (so that your boss does not have to waste his time interviewing candidates!) HINTS: - Use decision trees, and ID3 decision tree - Use Entropy to decide whether or not split a node, or on which attribute to split (unlike in class, where we used mostly GINI)