The problem presented is analyzing historical data of credit card transactions that were fraudulent and cards that weren’t fraudulent. The goal of this model is to predict future transactions as fraud. The model will be targeted to be optimized for precision rather than recall. Since it is important that this model identifies most of the fraud transactions with as much accuracy as possible.

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, we cannot provide the original features and more background information about the data. 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.

Dataset Source: https://www.kaggle.com/mlg-ulb/creditcardfraud

1. Clone the Repository into a folder that will be used for this project
2. Then if necessary install Anaconda Distribution in your programs files folder https://www.anaconda.com/distribution/
3. Search for Jupyter Notebook from Mac or Windows Cortana programm search bar
4. Then navigate to the project folder that contains the cloned repository and launch Ipython Notebook file(ipynb) for example