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The aim of the proposed methodology presented in this article is to select a set of genes whose mutations have been observed in certain cancers. For accurate analysis and proper identification, the dataset belong to both carcinogenic and normal state has been examined. The main focus is to pick out some genes whose variations is observed in experiment and can be considered the most significant. These genes might be termed as the genes having association with cancer as because their expression level has been observed as crucially changed from their initial state. Finding these genes can have contribution in different ways to biologists, medical practitioners, pathologists, and many more. As we know that all genes do not mutate in all cancers, so our target is to segregate the genes which are mutated notably from their original state. The method first used PCA to reduce number of features (genes) which overcomes the curse of dimensionality and then LR is used for binary classification and identifies the set of genes which are expressed differentially [22, 23].