Classification and regression are the important aspects of data mining. Data mining is the systematic procedure of extracting useful data from large datasets. Naïve Bayes and SVMs are useful for classification and regression. The naïve Bayes classifier is a typical generative classifier .while The SVM classifier is a typical Discriminative classifier .The naive Bayesian (NB) classifier is one of the simple yet powerful classification methods. It is considered as one of the most effectual and significant learning algorithms for machine learning and data mining and also has been treated as a core technique in information retrieval. Support Vector Machines (SVM) are supervised learning models with associated learning algorithm that analyses data and recognize patterns, used for classification and regression analysis. Here we attempt to analyze the performance both algorithms i.e. Naïve Bayes and SVMs through the development of useful application. With the accuracy of developed application this will going to estimate the performance. This paper presents the novel idea towards the classification of the naive bayes and SVMs algorithm by analyzing the human characteristics for the sake of their gender identification
