Python,Sklearn,Numpy ,Pandas
First we imported the dependencies i.e libraries and functions we need and loaded the csv file in pandas dataframe and then we got some information of the datatypes from first and last five rows from the dataframe and also check the number of missing values and then we found the distribution of data to find that the data is not balanced and then we distributed the data in some ligit and fradulent transaction and then we spliited our data into geatures and targets to feed it to our ML model and before training our model we splitted our data into training data and split data and then we evaluated our model based on the accuracy score.