In this notebook, I analyze the credit card dataset using python, pandas, and scikit-learn, and determine the best model to use to predict whether credit card transactions are fraudulent or not fraudulent, which could be helpful to a credit card company, so the company does not lose money to fraudulent transactions. However, if a company classifies too many transactions as fraud, when they are actually legitimate transactions, then the credit card company could irritate customers by needlessly denying their transactions.
Nearly all of the features have been anonymized, which means that I unfortunately won't be able to what features are most indicative of fraud. I was interested in attempting to solve this problem, because I have a business and accounting background and fraud is something that almost every organization has to deal with, at some level, and I thought this would be an interesting problem to solve.
The dataset used for this analysis was obtained from: https://www.kaggle.com/mlg-ulb/creditcardfraud
Chris Kelly - you can connect with me via LinkedIn (https://www.linkedin.com/in/chris-kelly-cpa-1b224450/) or email me at ckelly1325@gmail.com.