It is a CSV file, contains 31 features, the Class feature is used to classify the transaction whether it is a fraud or not.

• The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
• It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues original features are not provided and more background information about the data is also not present. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
• Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC), Matthews score, Cohen Kappa. Confusion matrix accuracy is not meaningful for unbalanced classification.

We have done Exploratory Data Analysis on full data then we have trained multiple classifier like Logistic Regression, SVM, Decision Tree, Random Forest, Gradient Boosting, xbg.

In order to run the project just download the data from above mentioned source then run any file.

You need to have installed following softwares and libraries in your machine before running this project.

• Python 3
• Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy.

• Python 3: https://www.python.org/downloads/
• Anaconda: https://www.anaconda.com/download/

• It may be possible to improve the random forest model by further tweaking the hyperparameters, given additional time and/or computational power.