Due to rapid growth in the field of various cashless transactions or digital transactions, credit cards are widely used in almost every work and hence there are more chances of fraudulent transactions. Depending on the type of fraud faced by banks or credit card companies, various measures can be adopted and implemented. Here the credit card fraud detection is based on fraudulent transactions. Generally, credit card fraud activities can happen both offline and online. But in today’s world online fraud transaction activities are increasing day by day. So in order to find online fraud transactions, various methods have been proposed and implemented by various organizations. This project proposes a machine learning technique for credit card fraud detection. Machine learning is the currently used technique implemented in various sectors. It is prioritized due to its ongoing advancements making our lives easier. In the proposed system, we use Random Forest Algorithm (RFA) for finding the fraudulent transactions and the accuracy of the transactions. The main aim is to detect fraud while making a transaction and alert the user if his/her account was accessed by an intruder. Credit card fraud detection using Machine learning is done by using classification and regression techniques. We use the Random Forest algorithm which is a supervised learning algorithm to classify the fraud card transaction into fraud or genuine transactions. This algorithm uses decision trees for classifying the data set. After classifying the dataset a confusion matrix is obtained. The performance of the Random Forest Algorithm is evaluated based on the confusion matrix. The results obtained from the processing data set to give an accuracy of about 70%. The random forest has better efficiency and accuracy than any other machine learning algorithm. This model is implemented by Python and SQL programming languages.