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Data mining nowadays became more and more important; it is used to extract knowledge from a large amount of data to help decision makers to make good decisions. Based on the type of the data and the type of the variable we want to predict, we choose the suitable algorithm, classification is one of the different algorithms used in data mining. In this survey paper, we first presented a brief overview about data mining and the data mining classification algorithms; then, we present classification algorithms used in data mining for prediction, and we present previous work and experiments using classification algorithms, such as Decision tree, Logistic regression and Naive Bayes. The aim is to give a clear insight of the classification algorithms and how they are used in several domains. [ABSTRACT FROM AUTHOR]

Employment is the main form of social integration, a factor of improving living conditions and preventing risks of poverty and vulnerability and the most appropriate indicator for assessing the level of social cohesion in a country. Graduates face every year real competitions to ensure their employability. We live now in a big data era, where the data is generated every day in large volumes, due to the use of technologies. But storing this data isn't the main objective, we need to use this data and take advantage of it by extracting valuable information from it in order to use it and improve a particular domain. Using data mining techniques will allow us to extract useful information and ameliorate employability by giving decision makers opportunities to make it better and predict in the future. In this paper, we present an intelligent system using data mining techniques on employability data, in a Big Data environment, which we used Hadoop ecosystem, and for data mining we used Rapid Miner Studio Educational Version 8.1.000. We presented first the characteristics of the proposed system, after that we presented the general architecture of the system and the tools and the technologies used, and finally we presented the system's process in details from the data collection till the results visualization. [ABSTRACT FROM AUTHOR]