Another post starts with you beautiful people! Hope you have learnt something from my previous post about machine learning classification real world problem Today we will continue our machine learning hands on journey and we will work on an interesting Credit Card Fraud Detection problem. The goal of this exercise is to anonymize credit card transactions labeled as fraudulent or genuine. For your own practice you can download the dataset from here- Download the dataset! About the dataset: 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. Let's start our analysis with loading the dataset first:- As per the official documentation - features V1, V2, ... V28 are the principal components obtained with
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