This method is based on the combination of results from different prediction models. In article two

The proposed model is presented in a model of model training using the data set containing

The subset of attributes is used with two data models of genetic algorithm and forest and in model

The second suggestion is the combination of training the neural network with the meta-algorithms and the first proposed model for production

We have used educational data sets. In fact, there are two separate processes in this model. One

The process for producing different training collections and the other task of training the neural network using

Hyper-market algorithms. In the models, the genetic algorithm and algorithm are used as two algorithms

We have used famous metropolitan areas to produce educational collections. Parameters affecting on

Classification of data in both models and model evaluation criteria TPR sensitivity TNR transparency and

The accuracy and accuracy of the tests are due to the large amount of data in the three important criteria

, Accuracy has been paid.