Classification using decision tree algorithm

Implementing a Decision learning algorithm, preferably ID3. Use this implementation to train a decision tree using the training data set which is provided in a .txt file and then test this decision on the test data set which again is a .txt file. Each row in both the .txt file is a sample. Output a prediction for each sample in the test data set file to an output file. Information about the data will be in another text file which will be provided together with the other 2 text files. ## Deliverables 1) Complete and fully-functional working program(s) in executable form as well as complete source code of all work done. 2) Deliverables must be in ready-to-run condition, as follows (depending on the nature of the deliverables): a) For web sites or other server-side deliverables intended to only ever exist in one place in the Buyer's environment--Deliverables must be installed by the Seller in ready-to-run condition in the Buyer's environment. b) For all others including desktop software or software the buyer intends to distribute: A software installation package that will install the software in ready-to-run condition on the platform(s) specified in this bid request. 3) All deliverables will be considered "work made for hire" under U.S. Copyright law. Buyer will receive exclusive and complete copyrights to all work purchased. (No GPL, GNU, 3rd party components, etc. unless all copyright ramifications are explained AND AGREED TO by the buyer on the site per the coder's Seller Legal Agreement). ## Platform Matlab prefered. May consider Java.