10.22376/ijpbs.2019.10.1.p1-12 Volume 8 Issue 2 2017 (April - June) Classification is a process which plays a vital role in the analysis of the gene expression data set. The paper focuses on variety of learning algorithms which are really challenging in nature. The proposed model has been implemented and evaluated by using 5 benchmark datasets and to evaluate the performance and throughput of the model, various learning algorithms has been used like Random Forest, Support vector Machine, K-Nearest Neighbor, Bayesian, Linear Discriminate, Multi layer Perception and Decision Tree. We proposed model by using the k –fold cross validation for training and testing of the data. DIVYA PATRA, SASHIKALA MISHRA, KAILASH SHAW AND KABERI DAS Classification; Gene Expression Data Set; Learning Algorithms. 389-394