Credit Card Fraud Prediction
In the banking domain, predicting credit card fraudulent activities is the key pressing issue, this article will explain how to identify fraud activities.
classification machine-learning visualization data-science exploratory-data-analysis random-forests logistic-regression decision-tree regression code article tutorial

Credit Card Fraud Prediction

In the banking domain, predicting the credit card fraudulent activities it the key pressing issue.

With the increase in technology, the way fraud is happing is also changed into new ways.

So these actives need to detect with machine learning or deep learning algorithms.

This article will build the famous credit card fraud detection model using the Kaggle credit card data.

We will also learn how to handle the imbalance problems.

For quick read checkout this link

Bellow are the learning outcome of this article.

Table of Contents * Why do we need to find fraud transactions? * Fraud Detection Approaches * What is Credit Card Fraud Detection? * Understanding of Credit Card Dataset * Data Explorations * Credit Card Data Preprocessing * Removing irrelevant columns/features * Checking null or nan values * Data Transformation * Splitting dataset * Building Credit Card Fraud Detection using Machine Learning algorithms * Decision Tree Algorithm Overview * Random Forest Algorithm Overview * Credit Card Fraud Detection with Decision Tree Algorithm * Decision tree algorithm Implementation using python sklearn library * Credit Card Fraud Detection with Random Forest Algorithm * Random forest algorithm Implementation using sklearn library * Why Accuracy not suitable for Data Imbalance Problems? * Suitable evaluation metrics for imbalanced data * Decision Tree Classification model results * Random Forest Classification model results * AUC and ROC Curves * Model Improvement Using Sampling Techniques * Applying Sampling Techniques * Decision tree classification after applying sampling techniques * Random Forest Tree Classifier after applying the sampling techniques * Conclusion * What next

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Data Scientist | Founder of dataaspirant.com
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