R Programming In Credit Card Fraud Detection

The paper should be 7 pages, double-spaced, 12 font-sizeexcluding the title and reference pages, using APA format, with at least 5 recent, scholarly, peer-reviewed references. As in any scholarly writing, students should not merely copy information from another author, but use evidence to support the contentions they have drawn from their findings and critically analyze related literature – each paper needs to be an analytical paper, not a summary of readings.
Below is the background about the research paper. 
The R programming language finds another application in detecting fraudulent credit card transactions. In this project, various Machine Learning algorithms are used that can differentiate counterfeit transactions from genuine ones. The credit card detection project in R makes use of multiple algorithms such as Logistic Regression, Decision Trees, Gradient Boosting Classifiers, and Artificial Neural Networks.
The Card Transactions dataset is used in this credit card fraud detection project in R; this dataset contains fraudulent as well as authentic transactions. The project has the following steps – importing the datasets containing the credit card transactions, exploring the data, manipulating and structuring the data, modeling the data, fitting the model in the Logistic Regression algorithm, and finally, implementing the Decision Tree, Artificial Neural Network, and Gradient Boosting models.
Based on the previously utilized dataset from your previous research paper, write a statistical analysis report that goes further in depth. Describe what you observe, what approaches you chose to take and why, and report any inferences that you come up with. If any data manipulation, statistical testing, or linear modeling has been performed, please include your functions and scripts in an appendix.

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