IoT The rapid growth in the E-Commerce industry has led to an exponential increase in theuse of credit cards for online purchases and consequently they have been surging in the fraud related to it. In recent years, it has become very difficult to detect fraud in credit card systems. For predicting these transactions banks make use of various machine learning methodologies, past datahas been collected and new features are being used for enhancing the predictive power. The performance of fraud detecting in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection techniques used. This paper investigatesthe performance of logistic regression, decision tree and random forest for credit card fraud detection. The three techniques are applied for the dataset and work is implemented in R language.