Here in this study, I will analyze the correlation of the buyers with their age and salary using various machine learning classifier algorithms. This study will predict, who will buy a new item faster as soon as it is launched in the market and how it will be related to the age and salary of the people, who are buying it. The aim of this study is to investigate six different types of Machine Learning, Classifier algorithms (namely Logistic Regression, SVM, Naive Bayes, KNN,Decision Tree, Random Forest and to show their comparative analysis and to predict whether a person will buy a certain product as soon as it is launched in the market. Experiments are performed on the Social_Network_Ads data set which is sourced from Kaggle the online community of data scientists and machine learning engineers. The performance of all the above algorithms is evaluated on the various metrics like recall, precision, F1_score and confusion matrix. Results are then compared.