The famous Black Scholes Option Pricing Model is a well-known option pricing model. Owing to some limitations it fails to perfectly detect the option price. In this study various regression and optimization techniques for predicting option price and analyzing various phenomena and properties with machine learning techniques for valuation and improving the accuracy of the option pricing model are used. The Proposed method is divided with different stages. Firstly, Principal Component Analysis

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... A) is used in order to identify the most influential inputs in the framework of the option pricing model and to reduce the dimensionality of our working data. Secondly, Support vector machine (SVM) and support vector regression (SVR) is used which is a very special type of learning algorithms characterized by the capacity of input variable as option price parameter and the use of the kernel functions. The combination of these two methods shows that SVM and PCA can perform better by consuming less time and memory. In this study, we investigate the estimation performance of option pricing model with SVM and PCA. A brief analysis of the accuracy of the approach also provided. The training of SVM and normalization of PCA is computed by MATLAB and it leads towards a new way for predicting option price perfectly if the formulation will be simulated using enough data.