Success of the software development companies is mostly dependent on the best effort prediction. If the predictedeffort is somewhat correct, then the company can find relief from the great tension of hurrying up the employees to getthe job done within targeted time. There are many estimation methods, techniques and tools that are available. But itis very difficult to select the best one for a particular project. Each method has its own advantages and disadvantages.And also the effort estimation depends on various parameters. It is the responsibility of the project manager to select thebest tool for his project. Based on the historical data, the project manager can find effort value of the new project afterapplying some statistical methods and data mining techniques on that data. The main aim of this work is to reveal howmuch accurate are data mining-classification techniques on software project effort prediction datasets when we performanalogy based effort estimation.